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This study develops a robust ragebait detection framework tailored for Japanese-language content on X, utilizing a labeled dataset created with the help of a large language model. The research reveals that ragebait is predominantly found in politically and socially contentious topics, and it significantly outpaces non-ragebait content in terms of speed of spread and negative emotional reactions. These findings not only validate the effectiveness of the detection method but also enhance understanding of the dynamics of emotionally provocative content online.
Ragebait is not just prevalent; it thrives in politically charged discussions, spreading faster and provoking stronger negative reactions than typical posts.
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.
