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Computer Science > Computation and Language

arXiv:2306.07201 (cs)
COVID-19 e-print

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[Submitted on 12 Jun 2023 (v1), last revised 13 Jun 2023 (this version, v2)]

Title:LTCR: Long-Text Chinese Rumor Detection Dataset

Authors:Ziyang Ma, Mengsha Liu, Guian Fang, Ying Shen
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Abstract: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. (this https URL)
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2306.07201 [cs.CL]
  (or arXiv:2306.07201v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2306.07201

Submission history

From: Guian Fang [view email]
[v1] Mon, 12 Jun 2023 16:03:36 UTC (972 KB)
[v2] Tue, 13 Jun 2023 08:08:18 UTC (971 KB)
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