# Susceptibility of Communities against Low-Credibility Content in Social News Websites

arXiv: https://arxiv.org/abs/2403.10705 · HTML: https://mlsanity.com/paper/2403.10705/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit](https://mlsanity.com/paper/2410.12061/) (2024)

## Abstract (arXiv)

Social news websites, such as Reddit, have evolved into prominent platforms for sharing and discussing news. A key issue on social news websites sites is the formation of echo chambers, which often lead to the spread of highly biased or uncredible news. We develop a method to identify communities within a social news website that are prone to uncredible or highly biased news. We employ a user embedding pipeline that detects user communities based on their stances towards posts and news sources. We then project each community onto a credibility-bias space and analyze the distributional characteristics of each projected community to identify those that have a high risk of adopting beliefs with low credibility or high bias. This approach also enables the prediction of individual users' susceptibility to low credibility content, based on their community affiliation. Our experiments show that latent space clusters effectively indicate the credibility and bias levels of their users, with significant differences observed across clusters -- a 34% difference in the users' susceptibility to low-credibility content and a 8.3% difference in the users' susceptibility to high political bias.