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arXiv Computation and Language

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

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.

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

The paper presents a large‑scale study of ragebait—content designed to provoke anger—on Japanese posts on X. It introduces a labeled dataset created with a large language model, trains Japanese language models, and builds an ensemble classifier that detects ragebait. Applying this detector to a vast dataset reveals that ragebait is especially common in politically and socially contentious topics, spreads faster, and elicits stronger negative emotions than non‑ragebait posts.

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arXiv Computation and Language
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arXiv AI
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Posts of Peril: Detecting Information About Hazards in Text

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arXiv Computation and Language
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Automatic Evaluation of Mental Health Stigma in Online Communication

The paper presents a new benchmark for automatically evaluating mental health stigma in online text, featuring a fine‑grained taxonomy that covers stigma mode, domain, and specific components across multiple mental health conditions. The authors annotate naturally occurring news and social media posts and test large language models and classifiers for sentiment, toxicity, and hate speech, finding that these models poorly capture stigma and often overpredict it without explicit rules. The benchmark, annotations, exemplar cases, and code are publicly released on GitHub.

By Naomi Baes, Jemima Kang, Nick Haslam, Chris Groot, Alsa Wu, Luc Raszewski, Yulia Otmakhova
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arXiv Computation and Language
2d ago

High prevalence of antagonizing rhetoric in trending topics on Twitter/X

The study introduces a computational method to distinguish between antagonizing and criticizing rhetoric in political discourse on Twitter/X. Analyzing over 17 million tweets from German trending topics, it finds that roughly one‑third contain antagonizing rhetoric, which is more prevalent in trending discussions than in general Twitter traffic. Right‑leaning users use antagonizing language more than left‑leaning users, and both forms of rhetoric are amplified within political camps, with certain parties—AfD and the Greens—emerging as primary antagonists.

By Armin Pournaki, Eckehard Olbrich
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