Susceptibility of Communities Against Low-Credibility Content in Social News Websites

Abstract

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.