Winds of Change: Impact of COVID-19 on Vaccine-related Opinions of Twitter users

• URL: http://arxiv.org/abs/2111.10667v1
• Date: Sat, 20 Nov 2021 19:33:51 GMT
• Title: Winds of Change: Impact of COVID-19 on Vaccine-related Opinions of Twitter users
• Authors: Soham Poddar, Mainack Mondal, Janardan Misra, Niloy Ganguly, Saptarshi Ghosh
• Abstract summary: 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.
• Score: 19.08902619892565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Understanding this online discourse is crucial for policy makers. This understanding is likely to impact the success of vaccination drives and might even impact the final outcome of our fight against the pandemic. The goal of this work is to improve this understanding using the lens of Twitter-discourse data. We first develop a classifier that categorizes users according to their vaccine-related stance with high precision (97%). Using this method we detect and investigate specific user-groups who posted about vaccines in pre-COVID and COVID times. Specifically, we identify distinct topics that these users talk about, and investigate how vaccine-related discourse has changed between pre-COVID times and COVID times. Finally, for the first time, we investigate the change of vaccine-related stances in Twitter users and shed light on potential reasons for such changes in stance. Our dataset and classifier are available at https://github.com/sohampoddar26/covid-vax-stance.

Related papers

• A Large-Scale Analysis of Persian Tweets Regarding Covid-19 Vaccination [1.2499537119440245]
  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. We offer a comprehensive analysis of public opinion in Iran about the Coronavirus vaccines using data gathered from Twitter.
  arXiv  Detail & Related papers  (2023-02-09T09:08:19Z)
• VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter [6.061534265076204]
  We create a new collection of over 3,101 tweets annotated with users' attitudes towards COVID-19 vaccination (stance) To the best of our knowledge, these are the first dataset and model that model vaccine hesitancy as a category distinct from pro- and anti-vaccine stance.
  arXiv  Detail & Related papers  (2023-01-17T02:00:31Z)
• 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)
• "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)
• 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)
• Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions [10.194753795363667]
  We conduct an in-depth analysis of tweets related to the coronavirus vaccine on Twitter. Results show that a majority of people are confident in the effectiveness of vaccines and are willing to get vaccinated. Negative tweets are often associated with the complaints of vaccine shortages, side effects after injections and possible death after being vaccinated.
  arXiv  Detail & Related papers  (2021-10-08T23:30:17Z)
• Insight from NLP Analysis: COVID-19 Vaccines Sentiments on Social Media [6.965634726663563]
  We collected tweet posts by the UK and US residents from the Twitter API during the pandemic. We performed sentiment analysis by VADER and proposed a new method that can count the individual's influence. The results indicated that celebrities could lead the opinion shift on social media in vaccination progress.
  arXiv  Detail & Related papers  (2021-06-08T03:37:22Z)
• 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.