Problem
Research questions and friction points this paper is trying to address.
Ragebait
Detection
Characterization
Online Content
Anger
From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X
🤖 AI Summary
研究开发了一种利用大型语言模型检测日语网络愤怒诱饵内容的框架，并分析了这类内容的特点及其在网络上的影响。
📝 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, 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Ragebait Detection
Large-scale Analysis
Japanese Language Models
Ensemble Classifier
Emotionally Provocative Content
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