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Joint rumour and stance identification based on semantic and structural information in social networks

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Abstract

Rumours that have spread in social networks have harmed society seriously, so rumour verification is a substantial task in social media analysis and natural language processing. In social networks, replies with different stances may provide direct clues to the veracity of the rumours. Thus, rumour verification would benefit from joint training with stance detection. However, there are still some shortcomings in current research, such as the unsatisfactory use of structure and semantic information in the conversation, features for different tasks independent of each other except for sharing input, and the insufficient discrimination of tweets with different stances. Aiming at these shortcomings, we first used the graph transformer to simultaneously obtain structural and semantic information such as dialogue reply, similar posts, same user, and same stance. Secondly, we adopted the partition filter network to explicitly model the rumour& stance-specific features and the shared interactive feature. Finally, we strengthened the discriminability of tweets with different stances through contrastive learning. Experiments on SemEval2017 and PHEME corpus show that the proposed model significantly improves the rumour and stance detection tasks.

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Data Availability

The SemEval-17 dataset analysed during the current study are available in the SemEval2017 repository, https://alt.qcri.org/semeval2017/task8/index.php?id=data-and-tools.

The PHEME dataset analysed during the current study are available in the PHEME repository, https://figshare.com/articles/dataset/PHEME_rumour_scheme_dataset_journalism_use_case/2068650.

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Funding

This work is supported by the National Key Research and Development Program of China (No. 2017YFC1200500), the National Natural Science Foundation of China (No. 61772378, 62176187), the Research Foundation of the Ministry of Education of China (No. 18JZD015).

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  1. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, Hubei, China

    Nanhang Luo, Dongdong Xie, Fei Li, Chong Teng & Donghong Ji

  2. School of Internaitonal Education, Wuhan University, Wuhan, Hubei, China

    Yiwen Mo

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  1. Nanhang Luo
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All authors contributed to the study conception and design. Material preparation, data analysis and model implementation were performed by Dongdong Xie, Yiwen Mo, Fei Li, Chong Teng and Donghong Ji. The first draft of the manuscript was written by Dongdong Xie and all authors commented on the manuscript.

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Luo, N., Xie, D., Mo, Y. et al. Joint rumour and stance identification based on semantic and structural information in social networks. Appl Intell 54, 264–282 (2024). https://doi.org/10.1007/s10489-023-05170-7

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