mlsanityData to 7 Oct 2026

Fringe News Networks: Dynamics of US News Viewership following the 2020 Presidential Election

arXiv 2101.10112 ↗First posted 22 Jan 2021cs.CY cs.CLcited by 8

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Abstract (arXiv)

The growing political polarization of the American electorate over the last several decades has been widely studied and documented. During the administration of President Donald Trump, charges of "fake news" made social and news media not only the means but, to an unprecedented extent, the topic of political communication. Using data from before the November 3rd, 2020 US Presidential election, recent work has demonstrated the viability of using YouTube's social media ecosystem to obtain insights into the extent of US political polarization as well as the relationship between this polarization and the nature of the content and commentary provided by different US news networks. With that work as background, this paper looks at the sharp transformation of the relationship between news consumers and here-to-fore "fringe" news media channels in the 64 days between the US presidential election and the violence that took place at US Capitol on January 6th. This paper makes two distinct types of contributions. The first is to introduce a novel methodology to analyze large social media data to study the dynamics of social political news networks and their viewers. The second is to provide insights into what actually happened regarding US political social media channels and their viewerships during this volatile 64 day period.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 15 of its 44 references that are in the map. First posted on arXiv 22 Jan 2021.

Builds on

15 of 44 references.

References · 15

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3 more
  1. 2018Inside the Right-Leaning Echo Chambers: Characterizing Gab, an Unmoderated Social Systemcited by 10
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 1; 2026: 1.

8 papers.

  1. 2023Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensivecited by 16
  2. 2024Infrastructure Ombudsman: Mining Future Failure Concerns from Structural Disaster Responsecited by 4
  3. 2023Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedingscited by 3
  4. 2023Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Samplingcited by 3
  5. 2025Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speechcited by 2
  6. 2023Understanding Political Polarisation using Language Models: A dataset and methodcited by 0
  7. 2024Community Needs and Assets: A Computational Analysis of Community Conversationscited by 0
  8. 2026Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotationcited by 0