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

arXiv: https://arxiv.org/abs/2403.04009 · HTML: https://mlsanity.com/paper/2403.04009/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 57 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 24 of 24)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder](https://mlsanity.com/paper/1609.08816/) (2016)
- [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020)
- [Botometer 101: Social bot practicum for computational social scientists](https://mlsanity.com/paper/2201.01608/) (2022)
- [Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US](https://mlsanity.com/paper/2106.01315/) (2021)
- [Winds of Change: Impact of COVID-19 on Vaccine-related Opinions of Twitter users](https://mlsanity.com/paper/2111.10667/) (2021)
- [VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter](https://mlsanity.com/paper/2301.06660/) (2023)
- [Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal](https://mlsanity.com/paper/2104.10635/) (2021)
- [Social Media Study of Public Opinions on Potential COVID-19 Vaccines: Informing Dissent, Disparities, and Dissemination](https://mlsanity.com/paper/2012.02165/) (2020)
- [Political Bias and Factualness in News Sharing across more than 100,000 Online Communities](https://mlsanity.com/paper/2102.08537/) (2021)
- [COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives](https://mlsanity.com/paper/2106.08423/) (2021)
- [Mechanisms and Attributes of Echo Chambers in Social Media](https://mlsanity.com/paper/2106.05401/) (2021)
- [Analysis of the influence of political polarization in the vaccination stance: the Brazilian COVID-19 scenario](https://mlsanity.com/paper/2110.03382/) (2021)
- ["This is Fake News": Characterizing the Spontaneous Debunking from Twitter Users to COVID-19 False Information](https://mlsanity.com/paper/2203.14242/) (2022)
- [Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies](https://mlsanity.com/paper/2112.10274/) (2021)
- [Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication](https://mlsanity.com/paper/2110.01746/) (2021)
- [Characterizing User Susceptibility to COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2109.09532/) (2021)
- [Local News Online and COVID in the U.S.: Relationships among Coverage, Cases, Deaths, and Audience](https://mlsanity.com/paper/2111.08515/) (2021)
- [Improving Vaccine Stance Detection by Combining Online and Offline Data](https://mlsanity.com/paper/2208.04491/) (2022)

## Built on by (most cited first, 7 of 7)

- [Regional Style and Color Transfer](https://mlsanity.com/paper/2404.13880/) (2024)
- [AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing](https://mlsanity.com/paper/2407.05633/) (2024)
- [Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models](https://mlsanity.com/paper/2406.17992/) (2024)
- [Estimating Causal Effects of Text Interventions Leveraging LLMs](https://mlsanity.com/paper/2410.21474/) (2024)
- [Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection](https://mlsanity.com/paper/2508.12632/) (2025)
- [Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learning](https://mlsanity.com/paper/2407.21264/) (2024)
- [The MediaSpin Dataset: Post-Publication News Headline Edits Annotated for Media Bias](https://mlsanity.com/paper/2412.02271/) (2024)

## Abstract (arXiv)

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.