Topic Modeling for Graph-Based Analysis of Fake News Diffusion

Fake news diffusion is a primary driver of misinformation. Analyzing deliberately false and misleading content is tough because social media platforms make it incredibly easy to create and spread huge amounts of information quickly. The intricate dynamics of fake news propagation demand the availability of ready-to-use frameworks for its analysis. This paper explores the automatic topic identification component of SPREADSHOT, a graph-based method designed to analyze fake news dissemination by examining two key factors: spreaders and topics. When it comes to news content, fake news frequently revolves around rapidly evolving topics due to its strong connection to current events. Consequently, topic modeling has gained significant traction for analyzing news articles. In our analysis, we explore two distinct topic modeling techniques: Latent Dirichlet Allocation (LDA) and BERTopic. While both offer valuable insights, we carefully justify which of these two techniques is best suited for integration into the SPREADSHOT framework for topic modeling.