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arXiv Computation and Language By Yupei Guo, Jiajun He, Xiaohan Shi, Tomoki Toda, Zekun Yang, Bowen Wang, Yukinobu Taniguchi

Leveraging LLM-Generated Explanations for Detecting Emotionally Rewritten Fake News

Read the original on arXiv Computation and Language →

The paper addresses fake news detection when articles are rewritten with different emotional tones while keeping factual claims unchanged. The authors create emotion‑rewritten test sets and generate explanations from original articles to serve as stable background knowledge. They propose a Gated Cross Attention (GCA) framework that integrates rewritten news with explanations, improving detection performance on PolitiFact and LUN datasets and remaining competitive on GossipCop.

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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