Susceptibility of Communities Against Low-Credibility Content in Social News Websites - Bayiz, Yigit Ege; Amini, Arash; Marculescu, Radu; Topcu, Ufuk (Proceedings of the International AAAI Conference on Web and Social Media, 2025)
Social news websites, such as Reddit, have evolved into prominent platforms for sharing and discussing news. A key issue on social news websites is the formation of low-credibility communities, which often lead to the spread of highly biased or uncredible news. We develop a method to identify communities prone to uncredible or highly biased news within a social news website. We employ a user embedding pipeline that detects user communities based on their stances toward 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 results show that latent space clusters effectively indicate the credibility and bias levels of their users, with significant variance 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.