Abstract

Fringe platforms like Gab harbor high volumes of hate speech due to minimal moderation and insular communities. Our study examines thefactors that determine how hate speech amplifies on these platforms. We prepared a novel dataset of 5K+ threads and 50K+ responses from four fringe platforms (Gab, 4chan, Stormfront, and Vanguard), including both structural features (e.g., timestamps, metadata) and con-tent features (e.g., original text, hate intensity of posts), where hate speech amplification was measured using platform-specific engagement metrics. We trained both Generalized Linear Models and Gradient Boosted Tree models to estimate how several features influence the amplification of hate speech on fringe platforms, and used Shapley value estimates to identify the relative importance of the features. Our analysis shows that research insights from social network analysis (SNA) of mainstream sites like X do not directly generalize to fringe platforms. For instance, our experiments show that using features like thread structure and disagreements in early response windows can give up to 74% lift in Root Mean Squared Error (RMSE) of predicting reply counts for hateful posts on fringe platforms, compared to a baseline model that has features like hate intensity and thread age (which would be considered predictive by regular SNA methods).