Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
80 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines (2306.13797v1)

Published 23 Jun 2023 in cs.SI and cs.CL

Abstract: Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes. The COVID-19 pandemic had fear and uncertainty about vaccines which has been well expressed on social media platforms such as Twitter. We analyse Twitter sentiments from the beginning of the COVID-19 pandemic and study the public behaviour during the planning, development and deployment of vaccines expressed in tweets worldwide using a sentiment analysis framework via deep learning models. In this way, we provide visualisation and analysis of anti-vaccine sentiments over the course of the COVID-19 pandemic. Our results show a link between the number of tweets, the number of cases, and the change in sentiment polarity scores during major waves of COVID-19 cases. We also found that the first half of the pandemic had drastic changes in the sentiment polarity scores that later stabilised which implies that the vaccine rollout had an impact on the nature of discussions on social media.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (86)
  1. A. E. Gorbalenya, S. C. Baker, R. S. Baric, R. J. de Groot, C. Drosten, A. A. Gulyaeva, B. L. Haagmans, C. Lauber, A. M. Leontovich, B. W. Neuman, D. Penzar, L. L. M. P. Stanley Perlman10, D. V. Samborskiy, I. A. Sidorov, I. Sola, and J. Ziebuhr, “The species severe acute respiratory syndrome-related coronavirus: classifying 2019-ncov and naming it sars-cov-2,” Nature Microbiology, vol. 5, no. 4, p. 536, 2020.
  2. V. Monteil, H. Kwon, P. Prado, A. Hagelkrüys, R. A. Wimmer, M. Stahl, A. Leopoldi, E. Garreta, C. H. Del Pozo, F. Prosper et al., “Inhibition of sars-cov-2 infections in engineered human tissues using clinical-grade soluble human ace2,” Cell, 2020.
  3. W. H. Organization et al., “Coronavirus disease 2019 (COVID-19): situation report, 72,” 2020.
  4. D. Cucinotta and M. Vanelli, “WHO declares COVID-19 a pandemic.” Acta bio-medica: Atenei Parmensis, vol. 91, no. 1, pp. 157–160, 2020.
  5. S. E. Jordan, S. E. Hovet, I. C.-H. Fung, H. Liang, K.-W. Fu, and Z. T. H. Tse, “Using twitter for public health surveillance from monitoring and prediction to public response,” Data, vol. 4, no. 1, 2019. [Online]. Available: https://www.mdpi.com/2306-5729/4/1/6
  6. A. Goel and L. Gupta, “Social media in the times of COVID-19,” Journal of Clinical Rheumatology, 2020.
  7. M. Cinelli, W. Quattrociocchi, A. Galeazzi, C. M. Valensise, E. Brugnoli, A. L. Schmidt, P. Zola, F. Zollo, and A. Scala, “The COVID-19 social media infodemic,” Scientific Reports, vol. 10, no. 1, pp. 1–10, 2020.
  8. S. Tasnim, M. M. Hossain, and H. Mazumder, “Impact of rumors and misinformation on covid-19 in social media,” Journal of preventive medicine and public health, vol. 53, no. 3, pp. 171–174, 2020.
  9. K. Sharma, S. Seo, C. Meng, S. Rambhatla, A. Dua, and Y. Liu, “Coronavirus on social media: Analyzing misinformation in twitter conversations,” arXiv preprint arXiv:2003.12309, 2020.
  10. A. Mian and S. Khan, “Coronavirus: the spread of misinformation,” BMC medicine, vol. 18, no. 1, pp. 1–2, 2020.
  11. O. D. Apuke and B. Omar, “Fake news and covid-19: modelling the predictors of fake news sharing among social media users,” Telematics and Informatics, vol. 56, p. 101475, 2021.
  12. A. Abd-Alrazaq, D. Alhuwail, M. Househ, M. Hamdi, and Z. Shah, “Top concerns of tweeters during the covid-19 pandemic: infoveillance study,” Journal of medical Internet research, vol. 22, no. 4, p. e19016, 2020.
  13. G. Barkur and G. B. K. Vibha, “Sentiment analysis of nationwide lockdown due to covid 19 outbreak: Evidence from india,” Asian journal of psychiatry, vol. 51, p. 102089, 2020.
  14. A. Karami and M. Anderson, “Social media and covid-19: Characterizing anti-quarantine comments on twitter,” Proceedings of the Association for Information Science and Technology, vol. 57, no. 1, p. e349, 2020.
  15. J. Kumar, M. S. Katto, A. A. Siddiqui, B. Sahito, M. Jamil, N. Rasheed, and M. Ali, “Knowledge, attitude, and practices of healthcare workers regarding the use of face mask to limit the spread of the new coronavirus disease (covid-19),” cureus, vol. 12, no. 4, 2020.
  16. T. Burki, “The online anti-vaccine movement in the age of covid-19,” The Lancet Digital Health, vol. 2, no. 10, pp. e504–e505, 2020.
  17. N. Puri, E. A. Coomes, H. Haghbayan, and K. Gunaratne, “Social media and vaccine hesitancy: new updates for the era of covid-19 and globalized infectious diseases,” Human Vaccines & Immunotherapeutics, pp. 1–8, 2020.
  18. S. Blume, “Anti-vaccination movements and their interpretations,” Social science & medicine, vol. 62, no. 3, pp. 628–642, 2006.
  19. A. Hussain, S. Ali, M. Ahmed, and S. Hussain, “The anti-vaccination movement: a regression in modern medicine,” Cureus, vol. 10, no. 7, 2018.
  20. A. Kata, “A postmodern pandora’s box: anti-vaccination misinformation on the internet,” Vaccine, vol. 28, no. 7, pp. 1709–1716, 2010.
  21. M. Ołpiński, “Anti-vaccination movement and parental refusals of immunization of children in usa,” Pediatria Polska, vol. 87, no. 4, pp. 381–385, 2012.
  22. O. Benecke and S. E. DeYoung, “Anti-vaccine decision-making and measles resurgence in the united states,” Global pediatric health, vol. 6, p. 2333794X19862949, 2019.
  23. A. de Figueiredo, C. Simas, E. Karafillakis, P. Paterson, and H. J. Larson, “Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study,” The Lancet, vol. 396, no. 10255, pp. 898–908, 2020.
  24. K. Megget, “Even covid-19 can’t kill the anti-vaccination movement,” BMJ, vol. 369, 2020.
  25. H. Yao, J.-H. Chen, and Y.-F. Xu, “Patients with mental health disorders in the covid-19 epidemic.” 2020.
  26. B. Pfefferbaum and C. S. North, “Mental health and the covid-19 pandemic,” New England Journal of Medicine, vol. 383, no. 6, pp. 510–512, 2020.
  27. W. Cullen, G. Gulati, and B. Kelly, “Mental health in the covid-19 pandemic,” QJM: An International Journal of Medicine, vol. 113, no. 5, pp. 311–312, 2020.
  28. W. H. Organization et al., “Mental health and psychosocial considerations during the COVID-19 outbreak, 18 March 2020,” World Health Organization, Tech. Rep., 2020.
  29. J. J. Liu, Y. Bao, X. Huang, J. Shi, and L. Lu, “Mental health considerations for children quarantined because of covid-19,” The Lancet Child & Adolescent Health, vol. 4, no. 5, pp. 347–349, 2020.
  30. D. W. Otter, J. R. Medina, and J. K. Kalita, “A survey of the usages of deep learning for natural language processing,” IEEE transactions on neural networks and learning systems, vol. 32, no. 2, pp. 604–624, 2020.
  31. S. Liu, N. Yang, M. Li, and M. Zhou, “A recursive recurrent neural network for statistical machine translation,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2014, pp. 1491–1500.
  32. S. M. Lakew, M. Cettolo, and M. Federico, “A comparison of transformer and recurrent neural networks on multilingual neural machine translation,” arXiv preprint arXiv:1806.06957, 2018.
  33. M. E. Basiri, S. Nemati, M. Abdar, E. Cambria, and U. R. Acharya, “Abcdm: An attention-based bidirectional cnn-rnn deep model for sentiment analysis,” Future Generation Computer Systems, vol. 115, pp. 279–294, 2021.
  34. D. Li and J. Qian, “Text sentiment analysis based on long short-term memory,” in 2016 First IEEE International Conference on Computer Communication and the Internet (ICCCI).   IEEE, 2016, pp. 471–475.
  35. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
  36. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
  37. S. Karita, N. Chen, T. Hayashi, T. Hori, H. Inaguma, Z. Jiang, M. Someki, N. E. Y. Soplin, R. Yamamoto, X. Wang et al., “A comparative study on transformer vs rnn in speech applications,” in 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).   IEEE, 2019, pp. 449–456.
  38. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
  39. M. C. Mills and J. Sivelä, “Should spreading anti-vaccine misinformation be criminalised?” bmj, vol. 372, 2021.
  40. N. F. Johnson, N. Velásquez, N. J. Restrepo, R. Leahy, N. Gabriel, S. El Oud, M. Zheng, P. Manrique, S. Wuchty, and Y. Lupu, “The online competition between pro-and anti-vaccination views,” Nature, vol. 582, no. 7811, pp. 230–233, 2020.
  41. J. Xue, J. Chen, C. Chen, C. Zheng, S. Li, and T. Zhu, “Public discourse and sentiment during the covid 19 pandemic: Using latent dirichlet allocation for topic modeling on twitter,” PloS one, vol. 15, no. 9, p. e0239441, 2020.
  42. M. Hung, E. Lauren, E. S. Hon, W. C. Birmingham, J. Xu, S. Su, S. D. Hon, J. Park, P. Dang, and M. S. Lipsky, “Social network analysis of covid-19 sentiments: application of artificial intelligence,” Journal of medical Internet research, vol. 22, no. 8, p. e22590, 2020.
  43. T. Wang, K. Lu, K. P. Chow, and Q. Zhu, “Covid-19 sensing: Negative sentiment analysis on social media in china via bert model,” Ieee Access, vol. 8, pp. 138 162–138 169, 2020.
  44. L. Tang, B. Bie, S.-E. Park, and D. Zhi, “Social media and outbreaks of emerging infectious diseases: A systematic review of literature,” American journal of infection control, vol. 46, no. 9, pp. 962–972, 2018.
  45. L. Mollema, I. A. Harmsen, E. Broekhuizen, R. Clijnk, H. De Melker, T. Paulussen, G. Kok, R. Ruiter, E. Das et al., “Disease detection or public opinion reflection ? content analysis of tweets, other social media, and online newspapers during the measles outbreak in The Netherlands in 2013,” Journal of medical Internet research, vol. 17, no. 5, p. e3863, 2015.
  46. S. C. Kim and K. H. Hawkins, “The psychology of social media communication in influencing prevention intentions during the 2019 US measles outbreak,” Computers in Human Behavior, vol. 111, p. 106428, 2020.
  47. K. Freberg, M. J. Palenchar, and S. R. Veil, “Managing and sharing H1N1 crisis information using social media bookmarking services,” Public relations review, vol. 39, no. 3, pp. 178–184, 2013.
  48. Q. Yang, H. Alamro, S. Albaradei, A. Salhi, X. Lv, C. Ma, M. Alshehri, I. Jaber, F. Tifratene, W. Wang et al., “Senwave: Monitoring the global sentiments under the covid-19 pandemic,” arXiv preprint arXiv:2006.10842, 2020.
  49. K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag, and A. E. Hassanien, “Sentiment analysis of covid-19 tweets by deep learning classifiers—a study to show how popularity is affecting accuracy in social media,” Applied Soft Computing, vol. 97, p. 106754, 2020.
  50. A. Cignarelli, A. Sansone, I. Caruso, S. Perrini, A. Natalicchio, L. Laviola, E. A. Jannini, and F. Giorgino, “Diabetes in the time of covid-19: A twitter-based sentiment analysis,” Journal of diabetes science and technology, vol. 14, no. 6, pp. 1131–1132, 2020.
  51. J. Zhou, S. Yang, C. Xiao, and F. Chen, “Examination of community sentiment dynamics due to covid-19 pandemic: A case study from australia,” arXiv preprint arXiv:2006.12185, 2020.
  52. B. P. Pokharel, “Twitter sentiment analysis during covid-19 outbreak in nepal,” Available at SSRN 3624719, 2020.
  53. C. de Las Heras-Pedrosa, P. Sánchez-Núñez, and J. I. Peláez, “Sentiment analysis and emotion understanding during the covid-19 pandemic in spain and its impact on digital ecosystems,” International Journal of Environmental Research and Public Health, vol. 17, no. 15, p. 5542, 2020.
  54. A. Kruspe, M. Häberle, I. Kuhn, and X. X. Zhu, “Cross-language sentiment analysis of european twitter messages duringthe covid-19 pandemic,” arXiv preprint arXiv:2008.12172, 2020.
  55. R. Lamsal, “Coronavirus (covid-19) tweets dataset,” 2020. [Online]. Available: https://dx.doi.org/10.21227/781w-ef42
  56. D. the Now, “Hydrator [computer software].” [Online]. Available: https://github.com/docnow/hydrator
  57. J. Lande, Y. Kaurav, C. Yu, and R. Chandra, “Global COVID-19 Twitter dataset,” 2022. [Online]. Available: https://www.kaggle.com/ds/2397387
  58. J. L. Elman and D. Zipser, “Learning the hidden structure of speech,” The Journal of the Acoustical Society of America, vol. 83, no. 4, pp. 1615–1626, 1988. [Online]. Available: http://link.aip.org/link/?JAS/83/1615/1
  59. J. L. Elman, “Finding structure in time,” Cognitive Science, vol. 14, pp. 179–211, 1990.
  60. P. J. Werbos, “Backpropagation through time: what it does and how to do it,” Proceedings of the IEEE, vol. 78, no. 10, pp. 1550–1560, 1990.
  61. R. Chandra, “Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction,” Neural Networks and Learning Systems, IEEE Transactions on, vol. 26, pp. 3123–3136, 2015.
  62. C. W. Omlin and C. L. Giles, “Constructing deterministic finite-state automata in recurrent neural networks,” J. ACM, vol. 43, no. 6, pp. 937–972, 1996.
  63. ——, “Training second-order recurrent neural networks using hints,” in Proceedings of the Ninth International Conference on Machine Learning.   Morgan Kaufmann, 1992, pp. 363–368.
  64. S. Hochreiter, “The vanishing gradient problem during learning recurrent neural nets and problem solutions,” Int. J. Uncertain. Fuzziness Knowl.-Based Syst., vol. 6, no. 2, pp. 107–116, 1998.
  65. A. Graves and J. Schmidhuber, “Framewise phoneme classification with bidirectional lstm and other neural network architectures,” Neural networks : the official journal of the International Neural Network Society, vol. 18, pp. 602–10, 07 2005.
  66. M. Schuster and K. Paliwal, “Bidirectional recurrent neural networks,” Signal Processing, IEEE Transactions on, vol. 45, pp. 2673 – 2681, 12 1997.
  67. Y. Fan, Y. Qian, F.-L. Xie, and F. K. Soong, “Tts synthesis with bidirectional lstm based recurrent neural networks,” in INTERSPEECH, 2014.
  68. A. Graves, N. Jaitly, and A.-r. Mohamed, “Hybrid speech recognition with deep bidirectional lstm,” in 2013 IEEE workshop on automatic speech recognition and understanding.   IEEE, 2013, pp. 273–278.
  69. R. Chandra and R. Saini, “Biden vs Trump: Modeling US general elections using BERT language model,” IEEE Access, vol. 9, pp. 128 494–128 505, 2021.
  70. R. Chandra and V. Kulkarni, “Semantic and sentiment analysis of selected bhagavad gita translations using bert-based language framework,” IEEE Access, vol. 10, pp. 21 291–21 315, 2022.
  71. R. Chandra and A. Krishna, “Covid-19 sentiment analysis via deep learning during the rise of novel cases,” PloS One, vol. 16, no. 8, p. e0255615, 2021.
  72. R. Chandra and M. Ranjan, “Artificial intelligence for topic modelling in hindu philosophy: Mapping themes between the Upanishads and the Bhagavad Gita,” Plos one, vol. 17, no. 9, p. e0273476, 2022.
  73. J. Lande, A. Pillay, and R. Chandra, “Deep learning for COVID-19 topic modelling via twitter: Alpha, delta and omicron,” arXiv preprint arXiv:2303.00135, 2023.
  74. P. F. Brown, V. J. Della Pietra, P. V. Desouza, J. C. Lai, and R. L. Mercer, “Class-based n-gram models of natural language,” Computational linguistics, vol. 18, no. 4, pp. 467–480, 1992.
  75. C. L. Baker, “Double negatives,” Linguistic inquiry, vol. 1, no. 2, pp. 169–186, 1970.
  76. J. Oldenburg, R. Klamroth, F. Langer, M. Albisetti, C. Von Auer, C. Ay, W. Korte, R. E. Scharf, B. Pötzsch, and A. Greinacher, “Diagnosis and management of vaccine-related thrombosis following astrazeneca COVID-19 vaccination: guidance statement from the GTH,” Hämostaseologie, vol. 41, no. 03, pp. 184–189, 2021.
  77. M. D. Knoll and C. Wonodi, “Oxford–AstraZeneca COVID-19 vaccine efficacy,” The Lancet, vol. 397, no. 10269, pp. 72–74, 2021.
  78. J. Wise, “COVID-19: European countries suspend use of Oxford-AstraZeneca vaccine after reports of blood clots,” 2021.
  79. A. A. Dror, N. Eisenbach, S. Taiber, N. G. Morozov, M. Mizrachi, A. Zigron, S. Srouji, and E. Sela, “Vaccine hesitancy: the next challenge in the fight against COVID-19,” European journal of epidemiology, vol. 35, pp. 775–779, 2020.
  80. È. Dubé, J. K. Ward, P. Verger, and N. E. MacDonald, “Vaccine hesitancy, acceptance, and anti-vaccination: trends and future prospects for public health,” Annu Rev Public Health, vol. 42, no. 1, pp. 175–91, 2021.
  81. R. A. Potamias, G. Siolas, and A.-G. Stafylopatis, “A transformer-based approach to irony and sarcasm detection,” Neural Computing and Applications, vol. 32, pp. 17 309–17 320, 2020.
  82. P. Verma, N. Shukla, and A. Shukla, “Techniques of sarcasm detection: A review,” in 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE).   IEEE, 2021, pp. 968–972.
  83. E. K. J. Dzisi and O. A. Dei, “Adherence to social distancing and wearing of masks within public transportation during the COVID 19 pandemic,” Transportation Research Interdisciplinary Perspectives, vol. 7, p. 100191, 2020.
  84. R. M. Kaplan and A. Milstein, “Influence of a COVID-19 vaccine’s effectiveness and safety profile on vaccination acceptance,” Proceedings of the National Academy of Sciences, vol. 118, no. 10, p. e2021726118, 2021.
  85. A. Singh, M. N. Halgamuge, and B. Moses, “An analysis of demographic and behavior trends using social media: Facebook, Twitter, and Instagram,” Social Network Analytics, p. 87, 2019.
  86. E. Ferrara, S. Cresci, and L. Luceri, “Misinformation, manipulation, and abuse on social media in the era of COVID-19,” Journal of Computational Social Science, vol. 3, pp. 271–277, 2020.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Rohitash Chandra (64 papers)
  2. Jayesh Sonawane (1 paper)
  3. Janhavi Lande (2 papers)
  4. Cathy Yu (1 paper)
Citations (4)
View on Semantic Scholar