Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media

• URL: http://arxiv.org/abs/2403.04009v1
• Date: Wed, 6 Mar 2024 19:41:02 GMT
• Title: Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media
• Authors: Bohan Jiang, Lu Cheng, Zhen Tan, Ruocheng Guo, Huan Liu
• Abstract summary: We analyze how inherent vaccine stances subtly influence individuals' selection of news sources and participation in social media discussions. We observe that individuals with moderate stances, particularly the vaccine-hesitant majority, are more vulnerable to the influence of PBN compared to those with extreme views.
• Score: 28.79984927305606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: News media has been utilized as a political tool to stray from facts, presenting biased claims without evidence. Amid the COVID-19 pandemic, politically biased news (PBN) has significantly undermined public trust in vaccines, despite strong medical evidence supporting their efficacy. In this paper, we analyze: (i) how inherent vaccine stances subtly influence individuals' selection of news sources and participation in social media discussions; and (ii) the impact of exposure to PBN on users' attitudes toward vaccines. In doing so, we first curate a comprehensive dataset that connects PBN with related social media discourse. Utilizing advanced deep learning and causal inference techniques, we reveal distinct user behaviors between social media groups with various vaccine stances. Moreover, we observe that individuals with moderate stances, particularly the vaccine-hesitant majority, are more vulnerable to the influence of PBN compared to those with extreme views. Our findings provide critical insights to foster this line of research.

Related papers

• Public Discourse about COVID-19 Vaccinations: A Computational Analysis of the Relationship between Public Concerns and Policies [3.203095675418499]
  With the rollout of vaccination campaigns, German-speaking regions exhibited much lower vaccination uptake than other European regions. We show that skepticism regarding the severity of the COVID-19 virus and towards efficacy and safety of vaccines were among the prevalent topics in the discourse on Twitter. During later phases of the pandemic, when implemented policies restricted the freedom of unvaccinated citizens, increased vaccination uptake could be observed.
  arXiv  Detail & Related papers  (2024-05-07T15:31:13Z)
• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)
• Suffering from Vaccines or from Government? : Partisan Bias in COVID-19 Vaccine Adverse Events Coverage [0.0]
  We find that conservative media are inclined to report adverse events more frequently than their liberal counterparts. The users who support the conservative opposing party were more likely to write the popular comments.
  arXiv  Detail & Related papers  (2022-11-19T14:17:07Z)
• 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)
• Dynamics and triggers of misinformation on vaccines [0.552480439325792]
  We analyze 6 years of Italian vaccine debate across diverse social media platforms (Facebook, Instagram, Twitter, YouTube) We first use the symbolic transfer entropy analysis of news production time-series to determine which category of sources, questionable or reliable, causally drives the agenda on vaccines. We then leverage deep learning models capable to accurately classify vaccine-related content based on the conveyed stance and discussed topic.
  arXiv  Detail & Related papers  (2022-07-25T15:35:48Z)
• A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter [4.696697601424039]
  We describe the collection and release of a dataset of tweets related to COVID-19 vaccines. This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, 17,934 of which are annotated with the originators' vaccination stances.
  arXiv  Detail & Related papers  (2022-06-27T13:44:48Z)
• "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)
• 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)
• Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
  We argue that it is critical to understand what user attributes potentially cause users to share fake news. In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities. We propose a principled approach to alleviating selection bias in fake news dissemination.
  arXiv  Detail & Related papers  (2020-10-20T19:37:04Z)
• 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.