← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-srw.45 · 记录 #1
Analyzing Hate Speech Amplification on Fringe Platforms
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
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).
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026-07 · 最新观测版本
Analyzing Hate Speech Amplification on Fringe Platforms
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).
查看此版本解读 →查看此版本原文 ↗