Echo platforms and partisan hashtags: mapping misinformation and thematic divides in the 2024 U.S. election

Social media platforms have deeply reshaped how information is produced, consumed, and trusted online, raising pressing concerns about the diffusion of misinformation and conspiratorial content, particularly during critical democratic moments such as elections. As ideologically motivated and deplatformed users migrate from mainstream networks to less regulated “alt-tech” spaces, these alternative environments could increasingly operate as ideologically homogeneous echo platforms, contributing to a broader fragmentation of the online information ecosystem. Drawing on two large publicly available datasets capturing activity on Truth Social and X (formerly Twitter) in the period of the attempted assassination of Donald Trump, this study analyzes the presence of questionable contents and thematic patterns based on hashtags. By constructing a bipartite user–hashtag network and applying a community detection algorithm, we identify how users group around specific hashtags and assess how thematic clusters vary in terms of informational reliability. Our findings show that Truth Social hosts a highly homogeneous discursive environment dominated by low-reliability sources and repetitive partisan messaging. Conversely, X reveals greater thematic heterogeneity and community-level fragmentation, where users' reliability scores align more distinctly with the topics they discuss. This study adds to the growing body of literature on platform-based misinformation dynamics by highlighting information diffusion and the thematic differences between mainstream and alt-tech environments.