# Shifting Polarization and Twitter News Influencers between two U.S. Presidential Elections

arXiv: https://arxiv.org/abs/2111.02505 · HTML: https://mlsanity.com/paper/2111.02505/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2021.

## Built on by (most cited first, 8 of 8)

- [The Perils & Promises of Fact-checking with Large Language Models](https://mlsanity.com/paper/2310.13549/) (2023)
- [Which linguistic cues make people fall for fake news? A comparison of cognitive and affective processing](https://mlsanity.com/paper/2312.03751/) (2023)
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- [The Politics of Language Choice: How the Russian-Ukrainian War Influences Ukrainians' Language Use on Twitter](https://mlsanity.com/paper/2305.02770/) (2023)
- [Involvement drives complexity of language in online debates](https://mlsanity.com/paper/2506.22098/) (2025)
- [Measuring Narrative Polarization in Online Discourse](https://mlsanity.com/paper/2601.07398/) (2026)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)
- [Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow](https://mlsanity.com/paper/2605.30400/) (2026)

## 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.