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Study characterizes ragebait on Japanese X using LLMs

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework for detecting and characterizing "ragebait" content on the Japanese X platform. This framework, built using large language models (LLMs) and Japanese language models, was applied to a large dataset of posts. The study found that ragebait is more common in discussions about politics, discrimination, and public health, and that such posts spread faster and elicit more negative emotional reactions. AI

RANK_REASON The cluster contains an academic paper detailing a study on online content.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Study characterizes ragebait on Japanese X using LLMs

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · 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

    arXiv:2609.02262v1 Announce Type: cross 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, hinder…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    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, …

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