How COVID-19 has Impacted the Anti-Vaccine Discourse: A Large-Scale Twitter Study Spanning Pre-COVID and Post-COVID Era (2404.01669v1)
Abstract: The debate around vaccines has been going on for decades, but the COVID-19 pandemic showed how crucial it is to understand and mitigate anti-vaccine sentiments. While the pandemic may be over, it is still important to understand how the pandemic affected the anti-vaccine discourse, and whether the arguments against non-COVID vaccines (e.g., Flu, MMR, IPV, HPV vaccines) have also changed due to the pandemic. This study attempts to answer these questions through a large-scale study of anti-vaccine posts on Twitter. Almost all prior works that utilized social media to understand anti-vaccine opinions considered only the three broad stances of Anti-Vax, Pro-Vax, and Neutral. There has not been any effort to identify the specific reasons/concerns behind the anti-vax sentiments (e.g., side-effects, conspiracy theories, political reasons) on social media at scale. In this work, we propose two novel methods for classifying tweets into 11 different anti-vax concerns -- a discriminative approach (entailment-based) and a generative approach (based on instruction tuning of LLMs) -- which outperform several strong baselines. We then apply this classifier on anti-vaccine tweets posted over a 5-year period (Jan 2018 - Jan 2023) to understand how the COVID-19 pandemic has impacted the anti-vaccine concerns among the masses. We find that the pandemic has made the anti-vaccine discourse far more complex than in the pre-COVID times, and increased the variety of concerns being voiced. Alarmingly, we find that concerns about COVID vaccines are now being projected onto the non-COVID vaccines, thus making more people hesitant in taking vaccines in the post-COVID era.
- Altman, J. D.; et al. 2023. Factors affecting vaccine attitudes influenced by the COVID-19 pandemic. Vaccines.
- Ameer, I.; et al. 2023. Multi-label emotion classification in texts using transfer learning. Expert Systems with Applications.
- Bonnevie, E.; et al. 2020. Quantifying the rise of vaccine opposition on Twitter during the COVID-19 pandemic. Journal of Communication in Healthcare.
- Burki, T. 2020. The online anti-vaccine movement in the age of COVID-19. The Lancet Digital Health.
- Chowdhury, J. R.; et al. 2020. Cross-lingual disaster-related multi-label tweet classification with manifold mixup. In Proc. ACL: Student Research Workshop.
- Chung, H. W.; et al. 2022. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416.
- Cotfas, L.-A.; et al. 2021. The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement. IEEE Access.
- Durmaz, N.; et al. 2022. The dramatic increase in anti-vaccine discourses during the COVID-19 pandemic: a social network analysis of Twitter. Human vaccines & immunotherapeutics.
- Fasce, A.; et al. 2023. A taxonomy of anti-vaccination arguments from a systematic literature review and text modelling. Nature Human Behaviour.
- Gunaratne, K.; et al. 2019. Temporal trends in anti-vaccine discourse on Twitter. Vaccine.
- Hu, E. J.; et al. 2021. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685.
- Hwang, J.; et al. 2022. Vaccine discourse during the onset of the COVID-19 pandemic: Topical structure and source patterns informing efforts to combat vaccine hesitancy. Plos one.
- Jarynowski, A.; et al. 2021. Mild adverse events of Sputnik V vaccine in Russia: social media content analysis of telegram via deep learning. Journal of Medical Internet Research.
- Kata, A. 2012. Anti-vaccine activists, Web 2.0, and the postmodern paradigm–An overview of tactics and tropes used online by the anti-vaccination movement. Vaccine.
- Knijff, M.; et al. 2023. Parental intention, attitudes, beliefs, trust and deliberation towards childhood vaccination in the Netherlands in 2022: Indications of change compared to 2013. medRxiv.
- Liu, Y.; et al. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
- Longpre, S.; et al. 2023. The Flan Collection: Designing Data and Methods for Effective Instruction Tuning. arXiv:2301.13688.
- Mathew, B.; et al. 2021. Hatexplain: A benchmark dataset for explainable hate speech detection. In Proc. AAAI.
- Mu, Y.; et al. 2023. VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter. In Proc. ICWSM.
- Mukherjee, R.; et al. 2021. Understanding the role of affect dimensions in detecting emotions from tweets: A multi-task approach. In Proc. SIGIR.
- Müller, M.; et al. 2020. Covid-twitter-bert: A natural language processing model to analyse covid-19 content on twitter. arXiv preprint arXiv:2005.07503.
- Müller, M. M.; et al. 2019. Crowdbreaks: tracking health trends using public social media data and crowdsourcing. Frontiers in public health.
- Pedregosa, F.; et al. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research.
- Poddar, S.; et al. 2022a. CAVES: A dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines. In Proc. SIGIR.
- Poddar, S.; et al. 2022b. Winds of Change: Impact of COVID-19 on Vaccine-related Opinions of Twitter users. In Proc. ICWSM.
- Praveen, S.; et al. 2021. Analyzing the attitude of Indian citizens towards COVID-19 vaccine-A text analytics study. Diabetes & Metabolic Syndrome: Clinical Research & Reviews.
- Raffel, C.; et al. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. JMLR.
- Reimers, N.; et al. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084.
- Rivera-Rivera, J. N.; et al. 2023. Attitudes towards HPV and COVID school-entry policies among adults living in Puerto Rico. Human Vaccines & Immunotherapeutics.
- Simig, D.; et al. 2022. Open vocabulary extreme classification using generative models. arXiv preprint arXiv:2205.05812.
- Entailment as few-shot learner. arXiv preprint arXiv:2104.14690.
- Wolf, T.; et al. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proc. EMNLP: System Demos.
- Zhang, Z.; et al. 2021. Explain and predict, and then predict again. In Proc. WSDM.
- Soham Poddar (9 papers)
- Rajdeep Mukherjee (23 papers)
- Subhendu Khatuya (5 papers)
- Niloy Ganguly (95 papers)
- Saptarshi Ghosh (82 papers)