From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X
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 classif...
| Authors | Zhiyang Qi, Kazuhiro Ito, Jinghui Chen et al. |
|---|---|
| Primary category | cs.SI |
| Published | 2 September 2026 |
| Last updated at source | 12 September 2026 |
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