LTCR: Long-Text Chinese Rumor Detection Dataset

Guian Fang; Mengsha Liu; Ying Shen; Ziyang Ma

LTCR: Long-Text Chinese Rumor Detection Dataset

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arxiv 2306.07201 v2 pith:T2OGVGDA submitted 2023-06-12 cs.CL

Ziyang Ma , Mengsha Liu , Guian Fang , Ying Shen This is my paper · ORCID

classification cs.CL

keywords fakenewsdatasetdetectionltcrchineseespeciallylong-text

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False information can spread quickly on social media, negatively influencing the citizens' behaviors and responses to social events. To better detect all of the fake news, especially long texts which are harder to find completely, a Long-Text Chinese Rumor detection dataset named LTCR is proposed. The LTCR dataset provides a valuable resource for accurately detecting misinformation, especially in the context of complex fake news related to COVID-19. The dataset consists of 1,729 and 500 pieces of real and fake news, respectively. The average lengths of real and fake news are approximately 230 and 152 characters. We also propose \method, Salience-aware Fake News Detection Model, which achieves the highest accuracy (95.85%), fake news recall (90.91%) and F-score (90.60%) on the dataset. (https://github.com/Enderfga/DoubleCheck)

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