How COVID-19 has Impacted the Anti-Vaccine Discourse: A Large-Scale Twitter Study Spanning Pre-COVID and Post-COVID Era

• URL: http://arxiv.org/abs/2404.01669v1
• Date: Tue, 2 Apr 2024 06:18:41 GMT
• Title: How COVID-19 has Impacted the Anti-Vaccine Discourse: A Large-Scale Twitter Study Spanning Pre-COVID and Post-COVID Era
• Authors: Soham Poddar, Rajdeep Mukherjee, Subhendu Khatuya, Niloy Ganguly, Saptarshi Ghosh,
• Abstract summary: The COVID-19 pandemic showed how crucial it is to understand and mitigate anti-vaccine sentiments. This study attempts to answer these questions through a large-scale study of anti-vaccine posts on Twitter. We propose two novel methods for classifying tweets into 11 different anti-vax concerns.
• Score: 23.47622466879736
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification [60.49594822215981]
  This paper presents a classification model for detecting COVID-19 vaccination related search queries. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task.
  arXiv  Detail & Related papers  (2022-12-16T13:57:41Z)
• Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy among Healthcare Workers [64.1526243118151]
  We find that doctors are overall more positive toward the COVID-19 vaccines. Doctors are more concerned with the effectiveness of the vaccines over newer variants. Nurses pay more attention to the potential side effects on children.
  arXiv  Detail & Related papers  (2022-09-11T14:22:16Z)
• Vaccine Discourse on Twitter During the COVID-19 Pandemic [0.7161783472741748]
  This study investigates posts related to COVID-19 vaccines on Twitter and focuses on those which have a negative stance toward vaccines. A dataset of 16,713,238 English tweets related to COVID-19 vaccines was collected. We show that the negativity with respect to COVID-19 vaccines has decreased over time along with the vaccine roll-outs.
  arXiv  Detail & Related papers  (2022-07-23T13:50:51Z)
• "COVID-19 was a FIFA conspiracy #curropt": An Investigation into the Viral Spread of COVID-19 Misinformation [60.268682953952506]
  We estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models. We provide a strategy to combat social media posts that are likely to cause widespread harm.
  arXiv  Detail & Related papers  (2022-06-12T19:41:01Z)
• Assessing the influence of French vaccine critics during the two first years of the COVID-19 pandemic [0.0]
  We look at the capacity of vaccine-critical activists to influence a wider public on social media during the COVID-19 epidemic. We analyze the evolution of debates over the COVID-19 vaccine on the French Twittosphere, during two first years of the pandemic. While debates over vaccines experienced a surge during this period, the share of vaccine-critical contents in these debates remains stable except for a limited number of short periods associated with specific events.
  arXiv  Detail & Related papers  (2022-02-22T14:47:41Z)
• Winds of Change: Impact of COVID-19 on Vaccine-related Opinions of Twitter users [19.08902619892565]
  Administering COVID-19 vaccines at a societal scale has been deemed as the most appropriate way to defend against the COVID-19 pandemic. This global vaccination drive naturally fueled a possibility of Pro-Vaxxers and Anti-Vaxxers strongly expressing their supports and concerns regarding the vaccines on social media platforms. The goal of this work is to improve this understanding using the lens of Twitter-discourse data.
  arXiv  Detail & Related papers  (2021-11-20T19:33:51Z)
• Vaccine skepticism detection by network embedding [0.0]
  We develop techniques to efficiently differentiate between pro-vaxxer and vax-skeptic content. We deploy several node embedding and community detection models that scale well for graphs with millions of edges.
  arXiv  Detail & Related papers  (2021-10-20T12:30:51Z)
• COVID-19 Vaccines: Characterizing Misinformation Campaigns and Vaccine Hesitancy on Twitter [8.181808709549227]
  We investigate misinformation and conspiracy campaigns and their characteristic behaviours for COVID-19 vaccines. We identify whether coordinated efforts are used to promote misinformation in vaccine related discussions. We study the large anti-vaccine misinformation community and smaller anti-vaccine communities, including a far-right anti-vaccine conspiracy group.
  arXiv  Detail & Related papers  (2021-06-15T20:32:10Z)
• The illicit trade of COVID-19 vaccines on the dark web [55.45786602961871]
  Early analyses revealed that dark web marketplaces (DWMs) started offering COVID-19 related products (e.g., masks and COVID-19 tests) as soon as the COVID-19 pandemic started. Here, we broaden the scope and depth of previous investigations by analysing 194 DWMs until July 2021, including the crucial period in which vaccines became available. We show that recreational drugs are the most affected among traditional DWMs product, with COVID-19 mentions steadily increasing since March 2020.
  arXiv  Detail & Related papers  (2021-02-10T14:52:54Z)
• Dark Web Marketplaces and COVID-19: before the vaccine [53.447910186085586]
  We analyse 851,199 listings extracted from 30 dark web marketplaces between January 1, 2020 and November 16, 2020. We identify 788 listings directly related to COVID-19 products and monitor the temporal evolution of product categories. We reveal how the online shadow economy has evolved during the COVID-19 pandemic and highlight the importance of a continuous monitoring of DWMs.
  arXiv  Detail & Related papers  (2020-08-04T14:27:41Z)
• Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter [65.7192861893042]
  We examine the extent to which the vaccination debate on Twitter is conductive to potential outreach to the vaccination hesitant. We discover that the vaccination skeptics, as well as the advocates, reside in their own distinct "echo chambers" At the center of these echo chambers we find the ardent supporters, for which we build highly accurate network- and content-based classifiers.
  arXiv  Detail & Related papers  (2020-03-26T13:55:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.