发表机构
The University of Tokyo（东京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发了愤怒诱饵检测框架，借助LLM构建标注数据集并训练日语检测模型，通过集成分类器分析日本X平台数据，发现愤怒诱饵在争议话题中更普遍且传播更快，贡献了其大规模特征表征。
AI 中文摘要
愤怒诱饵（ragebait）指旨在故意引发愤怒或愤慨、从而提升关注度与参与度的在线内容。然而，针对愤怒诱饵的可靠大规模检测与系统分析仍存在不足，这阻碍了人们理解其流行程度、影响及缓解措施的推进。本研究旨在开发有效的愤怒诱饵检测框架，并大规模明确愤怒诱饵的特征，为理解和缓解在线情绪化挑衅内容提供依据。我们借助大语言模型（LLM）构建了带标注的数据集，并训练了多个日语语言模型用于愤怒诱饵检测。随后，将所得集成分类器应用于日本X平台上的大规模日语帖子数据集。分析显示，愤怒诱饵在政治和社会争议性话题中更为普遍，包括政治、歧视、公共卫生及人际冲突。与非愤怒诱饵帖子相比，愤怒诱饵帖子传播更快，且获得更多负面反应，尤其是愤怒、恐惧、厌恶、悲伤和惊讶情绪。这些发现证明了所提检测器的实用性，并提供了日本在线话语中愤怒诱饵的大规模特征表征。
英文摘要
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, hindering efforts to understand its prevalence, impact, and mitigation. This study aims to develop an effective ragebait detection framework and to clarify the characteristics of ragebait at scale, providing a basis for understanding and mitigating emotionally provocative content online. We constructed a labeled dataset with the assistance of a large language model (LLM) and trained several Japanese language models for ragebait detection. The resulting ensemble classifier was then applied to a large-scale dataset of Japanese-language posts on X. Our analysis shows that ragebait is more prevalent in politically and socially contentious topics, including politics, discrimination, public health, and interpersonal conflict. Ragebait posts also spread faster and receive more negative reactions than non-ragebait posts, particularly anger, fear, disgust, sadness, and surprise. These findings demonstrate the utility of the proposed detector and provide a large-scale characterization of ragebait in Japanese online discourse.
CommentsAccepted at WI-IAT 2026
