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A writing style-based multi-task model with the hierarchical attention for rumor detection

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A Correction to this article was published on 27 July 2023

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Abstract

With the development of the Internet and social media, the harm caused by rumors has become more and more serious. Existing rumor detection methods focus on determining rumors by capturing their unusual textual content or communication structure, but fewer methods focus on the writing style of rumors. In order to identify rumors more effectively, we design and implement a multi-task rumor detection model with the hierarchical attention mechanism based on writing styles inspired by multi-task learning in this paper. The model combines a content-based rumor detection task and a writing style-based rumor detection task in a multi-task format, so that the two tasks can enhance their respective detection effects by interacting with each other during the model training process. In addition, we also use the hierarchical attention mechanism consisting of a word attention mechanism and a sentence attention mechanism to focus on words and posts that are more useful for rumor detection, which can reduce the interference of noise and further improve the detection accuracy. The experimental results of our model on the publicly available English Pheme dataset and Chinese Weibo dataset show that our model outperforms most of the existing better rumor detection methods.

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  1. http://blog.sina.com.cn/s/blog_b9d769f00101eohs.html.

  2. http://money.cnn.com/2016/11/17/technology/facebook-election-influence/.

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Acknowledgements

This work was supported in part by Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, China, under Grant No.2017SDSJ06 and the National Natural Science Foundation of China under Grant No. U1703261.

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Author notes

  1. Fangmin Dong, Mengyuan Wang and Guanghao Yang contributed equally to this work.

Authors and Affiliations

  1. Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, 443002, Yichang, Hubei, China

    Shuzhen Wan

  2. College of Computer and Information Technology, China Three Gorges University, 443002, Yichang, Hubei, China

    Shuzhen Wan, Bin Tang, Fangmin Dong, Mengyuan Wang & Guanghao Yang

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  1. Shuzhen Wan
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Correspondence to Shuzhen Wan.

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Wan, S., Tang, B., Dong, F. et al. A writing style-based multi-task model with the hierarchical attention for rumor detection. Int. J. Mach. Learn. & Cyber. 14, 3993–4008 (2023). https://doi.org/10.1007/s13042-023-01877-8

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