Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study
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Abstract (arXiv)
Background: Social media chatter in 2020 has been largely dominated by the COVID-19 pandemic. Existing research shows that COVID-19 discourse is highly politicized, with political preferences linked to beliefs and disbeliefs about the virus. As it happens with topics that become politicized, people may fall into echo chambers, which is the idea that one is only presented with information they already agree with, thereby reinforcing one's confirmation bias. Understanding the relationship between information dissemination and political preference is crucial for effective public health communication. Objective: We aimed to study the extent of polarization and examine the structure of echo chambers related to COVID-19 discourse on Twitter in the United States. Methods: First, we presented Retweet-BERT, a scalable and highly accurate model for estimating user polarity by leveraging language features and network structures. Then, by analyzing the user polarity predicted by Retweet-BERT, we provided new insights into the characterization of partisan users. Results: We observed that right-leaning users were noticeably more vocal and active in the production and consumption of COVID-19 information. We also found that most of the highly influential users were partisan, which may contribute to further polarization. Importantly, while echo chambers exist in both the right- and left-leaning communities, the right-leaning community was by far more densely connected within their echo chamber and isolated from the rest. Conclusions: We provided empirical evidence that political echo chambers are prevalent, especially in the right-leaning community, which can exacerbate the exposure to information in line with pre-existing users' views. Our findings have broader implications in developing effective public health campaigns and promoting the circulation of factual information online.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Mar 2021.
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Citing papers per year: 2022: 2; 2023: 4; 2024: 4; 2025: 1; 2026: 1.
12 papers.
- 2023Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformationcited by 18
- 2022Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networkscited by 13
- 2023Leveraging Large Language Models to Detect Influence Campaigns in Social Mediacited by 13
- 2025Mind the (Belief) Gap: Group Identity in the World of LLMscited by 12
- 2024Polarization of Autonomous Generative AI Agents Under Echo Chamberscited by 7
- 2022Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaignscited by 3
- 2024A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Healthcited by 1
- 2023Party Prediction for Twittercited by 0
- 2023Defensive Alliances in Signed Networkscited by 0
- 2024Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networkscited by 0
- 2024#EpiTwitter: Public Health Messaging During the COVID-19 Pandemiccited by 0
- 2026Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategiescited by 0
- 2026Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies12 Jan 2026
- 2025Mind the (Belief) Gap: Group Identity in the World of LLMs3 Mar 2025
- 2024A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health4 Nov 2024
- 2024#EpiTwitter: Public Health Messaging During the COVID-19 Pandemic4 Jun 2024
- 2024Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks16 Apr 2024
- 2024Polarization of Autonomous Generative AI Agents Under Echo Chambers19 Feb 2024
- 2023Leveraging Large Language Models to Detect Influence Campaigns in Social Media14 Nov 2023
- 2023Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformation13 Oct 2023
- 2023Defensive Alliances in Signed Networks13 Sep 2023
- 2023Party Prediction for Twitter25 Aug 2023
- 2022Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks18 Jul 2022
- 2022Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns19 Jan 2022