LTCR: Long-Text Chinese Rumor Detection Dataset

Ziyang Ma, Mengsha Liu, Guian Fang, Ying Shen
6/12/2023
cs.CL

Official code: Enderfga/DoubleCheck (23 stars, last commit 2023-06-13).

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. (https://github.com/Enderfga/DoubleCheck)

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Code Implementations(1)

Enderfga/DoubleCheckOfficial100%

LTCR: Long-Text Chinese Rumor Detection Dataset

232Python, Jupyter NotebookJun 12, 20233 years ago

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Cite this paper

@article{ma2023ltcr,
  title  = {LTCR: Long-Text Chinese Rumor Detection Dataset},
  author = {Ziyang Ma and Mengsha Liu and Guian Fang and Ying Shen},
  year   = {2023},
  eprint = {2306.07201},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.07201v2}
}

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