# Political Bias and Factualness in News Sharing across more than 100,000 Online Communities

arXiv: https://arxiv.org/abs/2102.08537 · HTML: https://mlsanity.com/paper/2102.08537/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2021.

## Built on by (most cited first, 9 of 9)

- [Customized large language models can outperform Community Notes in correcting misinformation](https://mlsanity.com/paper/2403.11169/) (2024)
- [Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media](https://mlsanity.com/paper/2403.04009/) (2024)
- [GPT-4V(ision) as A Social Media Analysis Engine](https://mlsanity.com/paper/2311.07547/) (2023)
- [Computational Assessment of Hyperpartisanship in News Titles](https://mlsanity.com/paper/2301.06270/) (2023)
- [On Context-aware Detection of Cherry-picking in News Reporting](https://mlsanity.com/paper/2401.05650/) (2024)
- [Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites](https://mlsanity.com/paper/2501.09102/) (2025)
- [Long Live Fine-Tuning: Task-Specific Transformers Outperform Zero-Shot LLMs for Misinformation Response Classification on Reddit](https://mlsanity.com/paper/2606.04274/) (2026)
- [The Tag is the Signal: URL-Agnostic Credibility Scoring for Messages on Telegram](https://mlsanity.com/paper/2601.13294/) (2026)
- [Quantifying Political Partisanship for Cross-Platform Analyses](https://mlsanity.com/paper/2607.21842/) (2026)

## Abstract (arXiv)

As civil discourse increasingly takes place online, misinformation and the polarization of news shared in online communities have become ever more relevant concerns with real world harms across our society. Studying online news sharing at scale is challenging due to the massive volume of content which is shared by millions of users across thousands of communities. Therefore, existing research has largely focused on specific communities or specific interventions, such as bans. However, understanding the prevalence and spread of misinformation and polarization more broadly, across thousands of online communities, is critical for the development of governance strategies, interventions, and community design. Here, we conduct the largest study of news sharing on reddit to date, analyzing more than 550 million links spanning 4 years. We use non-partisan news source ratings from Media Bias/Fact Check to annotate links to news sources with their political bias and factualness. We find that, compared to left-leaning communities, right-leaning communities have 105% more variance in the political bias of their news sources, and more links to relatively-more biased sources, on average. We observe that reddit users' voting and re-sharing behaviors generally decrease the visibility of extremely biased and low factual content, which receives 20% fewer upvotes and 30% fewer exposures from crossposts than more neutral or more factual content. This suggests that reddit is more resilient to low factual content than Twitter. We show that extremely biased and low factual content is very concentrated, with 99% of such content being shared in only 0.5% of communities, giving credence to the recent strategy of community-wide bans and quarantines.