From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X
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Computer Science > Social and Information Networks
Title:From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X
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.
| Comments: | Accepted at WI-IAT 2026. This is the pre-camera-ready version |
| Subjects: | Social and Information Networks (cs.SI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.02262 [cs.SI] |
| (or arXiv:2609.02262v1 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.02262
arXiv-issued DOI via DataCite (pending registration)
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