Shifting Polarization and Twitter News Influencers between two U.S. Presidential Elections
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Abstract (arXiv)
Social media are decentralized, interactive, and transformative, empowering users to produce and spread information to influence others. This has changed the dynamics of political communication that were previously dominated by traditional corporate news media. Having hundreds of millions of tweets collected over the 2016 and 2020 U.S. presidential elections gave us a unique opportunity to measure the change in polarization and the diffusion of political information. We analyze the diffusion of political information among Twitter users and investigate the change of polarization between these elections and how this change affected the composition and polarization of influencers and their retweeters. We identify "influencers" by their ability to spread information and classify them into those affiliated with a media organization, a political organization, or unaffiliated. Most of the top influencers were affiliated with media organizations during both elections. We found a clear increase from 2016 to 2020 in polarization among influencers and among those whom they influence. Moreover, 75% of the top influencers in 2020 were not present in 2016, demonstrating that such status is difficult to retain. Between 2016 and 2020, 10% of influencers affiliated with media were replaced by center- or right-orientated influencers affiliated with political organizations and unaffiliated influencers.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Nov 2021.
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Citing papers per year: 2023: 3; 2024: 0; 2025: 2; 2026: 3.
8 papers.
- 2023The Perils & Promises of Fact-checking with Large Language Modelscited by 31
- 2023Which linguistic cues make people fall for fake news? A comparison of cognitive and affective processingcited by 3
- 2025Fair Voting Methods as a Catalyst for Democratic Resilience: A Trilogy on Legitimacy, Impact and AI Safeguardingcited by 1
- 2023The Politics of Language Choice: How the Russian-Ukrainian War Influences Ukrainians' Language Use on Twittercited by 0
- 2025Involvement drives complexity of language in online debatescited by 0
- 2026Measuring Narrative Polarization in Online Discoursecited by 0
- 2026Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategiescited by 0
- 2026Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflowcited by 0
- 2026Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow28 May 2026
- 2026Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies12 Jan 2026
- 2026Measuring Narrative Polarization in Online Discourse12 Jan 2026
- 2025Fair Voting Methods as a Catalyst for Democratic Resilience: A Trilogy on Legitimacy, Impact and AI Safeguarding19 Dec 2025
- 2025Involvement drives complexity of language in online debates27 Jun 2025
- 2023Which linguistic cues make people fall for fake news? A comparison of cognitive and affective processing2 Dec 2023
- 2023The Perils & Promises of Fact-checking with Large Language Models20 Oct 2023
- 2023The Politics of Language Choice: How the Russian-Ukrainian War Influences Ukrainians' Language Use on Twitter4 May 2023