COVID-19 Rumor Detection Based on Heterogeneous Graph Convolutional Network with Cross-Domain Contrastive Learning

Author:

Tang Siyi,Qian Zhong,Liu Chengwei,Li Peifeng,Zhu Qiaoming

Publisher

Springer Nature Singapore

Reference26 articles.

1. Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B.J., Wong, K.-F., Cha, M.: Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the 25th IJCAI, pp. 3818–3824 (2016)

2. Chen, T., Li, X., Yin, H., Zhang, J.: Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. In: PAKDD 2018, pp. 40–52 (2018)

3. Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T.: A convolutional approach for misinformation identification. In: Proceedings of the 26th IJCAI, pp. 3901–3907 (2017)

4. Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 549–556 (2020)

5. Wei, L., Hu, D., Zhou, W., Yue, Z., Hu, S.: Towards propagation uncertainty: edge-enhanced Bayesian graph convolutional networks for rumor detection. In: Proceedings of the ACL-IJCNLP 2021, pp. 3845–3854 (2021)

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