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arXiv Computation and Language By Zhiyang Qi, Kazuhiro Ito, Jinghui Chen, Hibiki Nakamura, Zhangxuan Chen, Erina Murata, Masaki Chujyo, Fujio Toriumi

From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X

Read the original on arXiv Computation and Language →

The paper presents a large‑scale study of ragebait on Japanese X, developing an ensemble classifier trained on a dataset labeled with the help of a large language model. The detector was applied to a vast collection of Japanese posts, revealing that ragebait is especially common in politically and socially contentious topics such as politics, discrimination, public health, and interpersonal conflict. Ragebait posts spread more quickly and elicit stronger negative emotions—anger, fear, disgust, sadness, and surprise—than non‑ragebait posts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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