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arXiv 最近完成扫描:2026-11-15 00:32 · 全站最近入库:2026-11-14 15:09各信源进度 →

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新论文 · v1arXiv:2611.12797

激进化管道:在线厌女现象的特征刻画与预测

The Radicalization Pipeline: Characterizing and Predicting Online Misogyny

Anna Bertani, Nathan Ratkiewicz, Alessandro Flammini, Filippo Menczer

AI 速读 基于摘要

该研究对参与过 Reddit r/incels 社区的用户进行纵向分析,作者报告其参与前后毒性言论与仇恨言论上升、活动领域转移,并称可用机器学习在参与前识别未来用户。

cs.SI

查看原文摘要

Social media platforms are known to shape and polarize user opinions and, increasingly, facilitate pathways toward ideological extremism. Among these trends, online misogynistic communities associ- ated with the incel (involuntary celibate) ideology have received growing scholarly attention. Previous work has primarily focused on ideological narratives and linguistic content within incel communities them- selves. However, much less is known about how user behaviors evolve over time, particularly before and after their engagement with incel spaces, and whether early signals can anticipate such transitions. In this study, we conduct a longitudinal analysis of Reddit users who participated in the r/incels community on Reddit. Our results show that engagement with the incel community is associated with a substantial in- crease in toxicity in both posts and comments, while hate increases particularly in posts after engagement. These changes extend beyond explicitly ideological communities: users who engage with the r/incels sub- reddit display elevated toxicity and hate speech even in subreddits related to leisure, entertainment, and everyday interests. In parallel, user activity shifts away from recreational and support-oriented communi- ties toward subreddits focused on social issues, indicating a broader reorientation of online participation. Finally, we show that future incel users can be identified with high accuracy prior to engagement using supervised machine learning models trained on linguistic, contextual, and interaction-based features. These findings suggest that online misogynistic radicalization follows detectable behavioral trajectories and diffuses across the broader social media ecosystem.

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1590ACL Anthology · ACL 2026

PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media

Zoher Kachwala, Bao Tran Truong, Rasika Muralidharan, Haewoon Kwak Indiana University, Jisun An Indiana University, Filippo Menczer Indiana University

原文摘要

Social media are shifting towards pluralism — community-governed platforms where groups define their own norms. What violates rules in one community may be perfectly acceptable in another. Can AI models help moderate such pluralistic communities? We formalize the task as a multiple-choice problem, mirroring how human moderators operate in the real world: given a comment and its surrounding context, identify which specific rule, if any, is violated. We introduce PluRule, a multimodal, multilingual benchmark for detecting 13,371 rule violations across 1,989 Reddit communities spanning 2,885 rules in 9 languages. Using this benchmark, we show that state-of-the-art vision-language models struggle significantly: even GPT-5.2 with high reasoning performs only slightly better than a trivial baseline. We also find that bigger models and increased context provide marginal gains, and universal rules like civility and self-promotion are easier to detect. Our results show that moderation of pluralistic communities on social media is a fundamental challenge for language models. Our code and benchmark are publicly available.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表