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A Large-Scale Analysis of Persian Tweets Regarding Covid-19 Vaccination (2302.04511v3)
Published 9 Feb 2023 in cs.CL and cs.SI
Abstract: The Covid-19 pandemic had an enormous effect on our lives, especially on people's interactions. By introducing Covid-19 vaccines, both positive and negative opinions were raised over the subject of taking vaccines or not. In this paper, using data gathered from Twitter, including tweets and user profiles, we offer a comprehensive analysis of public opinion in Iran about the Coronavirus vaccines. For this purpose, we applied a search query technique combined with a topic modeling approach to extract vaccine-related tweets. We utilized transformer-based models to classify the content of the tweets and extract themes revolving around vaccination. We also conducted an emotion analysis to evaluate the public happiness and anger around this topic. Our results demonstrate that Covid-19 vaccination has attracted considerable attention from different angles, such as governmental issues, safety or hesitancy, and side effects. Moreover, Coronavirus-relevant phenomena like public vaccination and the rate of infection deeply impacted public emotional status and users' interactions.
- Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022. http://dx.doi.org/10.1162/jmlr.2003.3.4-5.993, URL http://portal.acm.org/citation.cfm?id=944937 Bonnevie et al (2020) Bonnevie E, Goldbarg J, Gallegos-Jeffrey AK, et al (2020) Content themes and influential voices within vaccine opposition on twitter, 2019. American Journal of Public Health 110(S3):S326–S330. 10.2105/AJPH.2020.305901, URL https://doi.org/10.2105/AJPH.2020.305901, pMID: 33001733, https://arxiv.org/abs/https://doi.org/10.2105/AJPH.2020.305901 Bonnevie et al (2021a) Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, et al (2021a) Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. Journal of Communication in Healthcare 14(1):12–19. 10.1080/17538068.2020.1858222 Bonnevie et al (2021b) Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, et al (2021b) Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. Journal of Communication in Healthcare 14(1):12–19. 10.1080/17538068.2020.1858222, URL https://doi.org/10.1080/17538068.2020.1858222, https://arxiv.org/abs/https://doi.org/10.1080/17538068.2020.1858222 Cascini et al (2022) Cascini F, Pantovic A, Al-Ajlouni YA, et al (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine Chang et al (2009) Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Bonnevie E, Goldbarg J, Gallegos-Jeffrey AK, et al (2020) Content themes and influential voices within vaccine opposition on twitter, 2019. American Journal of Public Health 110(S3):S326–S330. 10.2105/AJPH.2020.305901, URL https://doi.org/10.2105/AJPH.2020.305901, pMID: 33001733, https://arxiv.org/abs/https://doi.org/10.2105/AJPH.2020.305901 Bonnevie et al (2021a) Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, et al (2021a) Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. Journal of Communication in Healthcare 14(1):12–19. 10.1080/17538068.2020.1858222 Bonnevie et al (2021b) Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, et al (2021b) Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. Journal of Communication in Healthcare 14(1):12–19. 10.1080/17538068.2020.1858222, URL https://doi.org/10.1080/17538068.2020.1858222, https://arxiv.org/abs/https://doi.org/10.1080/17538068.2020.1858222 Cascini et al (2022) Cascini F, Pantovic A, Al-Ajlouni YA, et al (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine Chang et al (2009) Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, et al (2021a) Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. Journal of Communication in Healthcare 14(1):12–19. 10.1080/17538068.2020.1858222 Bonnevie et al (2021b) Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, et al (2021b) Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. Journal of Communication in Healthcare 14(1):12–19. 10.1080/17538068.2020.1858222, URL https://doi.org/10.1080/17538068.2020.1858222, https://arxiv.org/abs/https://doi.org/10.1080/17538068.2020.1858222 Cascini et al (2022) Cascini F, Pantovic A, Al-Ajlouni YA, et al (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine Chang et al (2009) Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, et al (2021b) Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. Journal of Communication in Healthcare 14(1):12–19. 10.1080/17538068.2020.1858222, URL https://doi.org/10.1080/17538068.2020.1858222, https://arxiv.org/abs/https://doi.org/10.1080/17538068.2020.1858222 Cascini et al (2022) Cascini F, Pantovic A, Al-Ajlouni YA, et al (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine Chang et al (2009) Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Cascini F, Pantovic A, Al-Ajlouni YA, et al (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine Chang et al (2009) Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. 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URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. 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Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. 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In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. 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Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, et al (2021b) Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. Journal of Communication in Healthcare 14(1):12–19. 10.1080/17538068.2020.1858222, URL https://doi.org/10.1080/17538068.2020.1858222, https://arxiv.org/abs/https://doi.org/10.1080/17538068.2020.1858222 Cascini et al (2022) Cascini F, Pantovic A, Al-Ajlouni YA, et al (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine Chang et al (2009) Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Cascini F, Pantovic A, Al-Ajlouni YA, et al (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine Chang et al (2009) Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, et al (2021b) Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. Journal of Communication in Healthcare 14(1):12–19. 10.1080/17538068.2020.1858222, URL https://doi.org/10.1080/17538068.2020.1858222, https://arxiv.org/abs/https://doi.org/10.1080/17538068.2020.1858222 Cascini et al (2022) Cascini F, Pantovic A, Al-Ajlouni YA, et al (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine Chang et al (2009) Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Cascini F, Pantovic A, Al-Ajlouni YA, et al (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine Chang et al (2009) Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. 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Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. 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URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. 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URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Cascini F, Pantovic A, Al-Ajlouni YA, et al (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine Chang et al (2009) Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. 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Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. 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Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. 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International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. 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URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Cascini F, Pantovic A, Al-Ajlouni YA, et al (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine Chang et al (2009) Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, et al (eds) Advances in Neural Information Processing Systems, vol 22. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf Chopra et al (2021) Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. 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URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. 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Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Chopra H, Vashishtha A, Pal R, et al (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR abs/2104.01131. URL https://arxiv.org/abs/2104.01131, https://arxiv.org/abs/2104.01131 Conneau et al (2019) Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Conneau A, Khandelwal K, Goyal N, et al (2019) Unsupervised cross-lingual representation learning at scale. CoRR abs/1911.02116. URL http://arxiv.org/abs/1911.02116, https://arxiv.org/abs/1911.02116 Devlin et al (2018) Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. 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URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. 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Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. 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International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Devlin J, Chang M, Lee K, et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. URL http://arxiv.org/abs/1810.04805, https://arxiv.org/abs/1810.04805 Dodds et al (2011) Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Dodds PS, Harris KD, Kloumann IM, et al (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. 10.1371/journal.pone.0026752, URL https://doi.org/10.1371/journal.pone.0026752 Durmaz and Hengirmen (2022) Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. 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Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccines & Immunotherapeutics 18(1):2025,008. 10.1080/21645515.2021.2025008, pMID: 35113767 Farahani et al (2021) Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Farahani M, Gharachorloo M, Farahani M, et al (2021) Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. 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URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. 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URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. 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Neural Processing Letters 10.1007/s11063-021-10528-4 Filter (2022) Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022] HAZM (2018) HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. 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Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. 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URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022] Hinton et al (2015) Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. 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URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. 10.48550/ARXIV.1503.02531, URL https://arxiv.org/abs/1503.02531 Hosseini et al (2020) Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. 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URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR abs/2005.08400. URL https://arxiv.org/abs/2005.08400, https://arxiv.org/abs/2005.08400 Hutto and Gilbert (2014) Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. 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Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. URL https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Khan (2014) Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. 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URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Khan S (2014) Qualitative research method: Grounded theory. International Journal of Business and Management 9. 10.5539/ijbm.v9n11p224 Kharazi (2021) Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022] Kwok et al (2021) Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. 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URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of medical Internet research 23(5):e26,953 Lan et al (2019) Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lan Z, Chen M, Goodman S, et al (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs/1909.11942. URL http://arxiv.org/abs/1909.11942, https://arxiv.org/abs/1909.11942 Le et al (2020) Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. 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URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. 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URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. 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J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Le TT, Andreadakis Z, Kumar A, et al (2020) The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery 19(5):305–306. 10.1038/d41573-020-00073-5, URL https://doi.org/10.1038/d41573-020-00073-5 Liu et al (2019) Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. 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URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Liu Y, Ott M, Goyal N, et al (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692. URL http://arxiv.org/abs/1907.11692, https://arxiv.org/abs/1907.11692 Lyu et al (2022) Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. 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URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Lyu H, Wang J, Wu W, et al (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intelligent medicine 2(1):1–12 Lyu et al (2021) Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine–related discussion on twitter: Topic modeling and sentiment analysis. J Med Internet Res 23(6):e24,435. 10.2196/24435, URL https://www.jmir.org/2021/6/e24435 Newman et al (2010) Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. 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Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. 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International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Newman D, Lau J, Grieser K, et al (2010) Automatic evaluation of topic coherence. pp 100–108 Nezhad and Deihimi (2022) Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Nezhad ZB, Deihimi MA (2022) Analyzing iranian opinions toward covid-19 vaccination. IJID Regions https://doi.org/10.1016/j.ijregi.2021.12.011, URL https://www.sciencedirect.com/science/article/pii/S2772707622000030 Organization (2021) Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022] Sahu et al (2014) Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. 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Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. 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Indian journal of psychological medicine 36(1):77–79 Sanh et al (2019) Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. 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URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. 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Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108. URL http://arxiv.org/abs/1910.01108, https://arxiv.org/abs/1910.01108 Shokrollahi et al (2021) Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. 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Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. 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Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR abs/2109.00298. URL https://arxiv.org/abs/2109.00298, https://arxiv.org/abs/2109.00298 Smedt and Daelemans (2012) Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Smedt TD, Daelemans W (2012) Pattern for python. Journal of Machine Learning Research 13(66):2063–2067. URL http://jmlr.org/papers/v13/desmedt12a.html Thelwall et al (2021) Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). 10.3145/epi.2021.mar.12, URL https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322 Troiano and Nardi (2021) Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025, URL https://www.sciencedirect.com/science/article/pii/S0033350621000834 Villavicencio et al (2021) Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Villavicencio C, Macrohon JJ, Inbaraj XA, et al (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information 12(5). 10.3390/info12050204, URL https://www.mdpi.com/2078-2489/12/5/204 Wicke and Bolognesi (2021) Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Frontiers in Communication 6. 10.3389/fcomm.2021.651997, URL https://www.frontiersin.org/article/10.3389/fcomm.2021.651997 Yang et al (2019) Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Yang Z, Dai Z, Yang Y, et al (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR abs/1906.08237. URL http://arxiv.org/abs/1906.08237, https://arxiv.org/abs/1906.08237 Yin and Wang (2014) Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, 10.1145/2623330.2623715, URL https://doi.org/10.1145/2623330.2623715 Yousefinaghani et al (2021) Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Yousefinaghani S, Dara R, Mubareka S, et al (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059, URL https://www.sciencedirect.com/science/article/pii/S1201971221004628 Zacharias (2020) Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022] Zhan et al (2015) Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725 Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
- Zhan J, Ren J, Fan J, et al (2015) Distinctive effects of fear and sadness induction on anger and aggressive behavior. Frontiers in Psychology 6:725
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