Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections
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Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.
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Real and present concerns exist over the role of mis- and disinformation in public discourse, the emergence of echo chambers, and deepening political polarization, in particular around big societal issues like climate change.
“Real and present concerns exist over the role of mis- and disinformation in public discourse (116–118), the emergence of echo chambers (119) and deepening political polarization (120, 121), in particular around big societal issues like climate change (122).”
From The benefits and dangers of anthropomorphic conversational agents (2025) · cites this paper as [121]
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- Authors
- Flamino, J., Galeazzi, A., Feldman, S., Macy, M. W., Cross, B., Zhou, Z., Serafino, M., Bovet, A., Makse, H. A., & Szymanski, B. K.
- Affiliations
- Rensselaer Polytechnic Institute (US)
- Ca' Foscari University of Venice (IT)
- Cornell University (US)
- Capital University of Economics and Business (CN)
- City College of New York (US)
- University of Zurich (CH)
- Corresponding author
- Makse, Hernán A.
- Szymanski, Boleslaw K.
- ORCID
- Venue
- Nature Human Behaviour
- Published
- 2023-03-13
- Volume
- 7
- Issue
- 6
- Pages
- 904-916
- ISSN
- 2397-3374 (ISSN-L)
- PMC
- PMC10289895
- OpenAlex
- W4324130084
- Cited by
- 113
- References
- 54
- Citations by year
- 2026: 13 · 2025: 48 · 2024: 39 · 2023: 12 · 2022: 1
- FWCI
- 48.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Human Behaviour (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by-nc-nd)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Science Foundation (BSE-2214216, SES-2049207, BSE-2214217)
- United States Department of Defense | Defense Advanced Research Projects Agency (HR001121C0165)
- Beijing Municipal Commission of Education (KM202210038002)
- U.S. Department of Health & Human Services | National Institutes of Health (R01 EB028157)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Keywords
- political polarization, Twitter, news media, 2016 US presidential election, 2020 US presidential election, political bias, fact-checking, fake news, echo chambers, social media influencers, information dissemination, ideological polarization
- MeSH terms
- Communication, Humans, Mass Media, Politics, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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