English(EN) From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X

研究表征日本X平台上的仇恨诱饵，发现其在争议性话题中普遍存在

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员开发了一个新的框架，用于检测和表征日本X平台上的仇恨诱饵，利用大型语言模型创建了一个标记数据集。他们的分析显示，仇恨诱饵在与政治、歧视、公共卫生和人际冲突相关的讨论中更为常见。研究还发现，与非仇恨诱饵内容相比，仇恨诱饵帖子的传播速度更快，并引发更强烈的负面情绪反应。 AI

影响为理解煽动性在线内容的性质和传播提供了见解，可能为内容审核策略提供信息。

排序理由学术论文，详细介绍一项关于在线内容的研究。[lever_c_demoted from research: ic=1 ai=0.4]

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本文如何被排名

Signal score: 8 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文，详细介绍一项关于在线内容的研究。[lever_c_demoted from research: ic=1 ai=0.4]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: Standard; On-topic for AI-industry coverage; kept in the public index.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

arXiv cs.CL TIER_1 English(EN) · Zhiyang Qi, Kazuhiro Ito, Jinghui Chen, Hibiki Nakamura, Zhangxuan Chen, Erina Murata, Masaki Chujyo, Fujio Toriumi · 2026-09-03 04:00

从检测到特征描述：一项关于日本X平台“激怒诱饵”的大规模研究

arXiv:2609.02262v1 Announce Type: cross Abstract: Ragebait refers to online content intentionally designed to provoke anger or outrage and thereby increase attention and engagement. However, reliable large-scale detection and systematic analysis of ragebait remain limited, hinder…