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Hy-DeFake: Hypergraph Neural Networks for Detecting Fake News in Online Social Networks (2309.02692v2)

Published 6 Sep 2023 in cs.SI

Abstract: Nowadays social media is the primary platform for people to obtain news and share information. Combating online fake news has become an urgent task to reduce the damage it causes to society. Existing methods typically improve their fake news detection performances by utilizing textual auxiliary information (such as relevant retweets and comments) or simple structural information (i.e., graph construction). However, these methods face two challenges. First, an increasing number of users tend to directly forward the source news without adding comments, resulting in a lack of textual auxiliary information. Second, simple graphs are unable to extract complex relations beyond pairwise association in a social context. Given that real-world social networks are intricate and involve high-order relations, we argue that exploring beyond pairwise relations between news and users is crucial for fake news detection. Therefore, we propose constructing an attributed hypergraph to represent non-textual and high-order relations for user participation in news spreading. We also introduce a hypergraph neural network-based method called Hy-DeFake to tackle the challenges. Our proposed method captures semantic information from news content, credibility information from involved users, and high-order correlations between news and users to learn distinctive embeddings for fake news detection. The superiority of Hy-DeFake is demonstrated through experiments conducted on four widely-used datasets, and it is compared against eight baselines using four evaluation metrics.

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References (53)
  1. M. S. Islam, T. Sarkar, S. H. Khan, A.-H. M. Kamal, S. M. Hasan, A. Kabir, D. Yeasmin, M. A. Islam, K. I. A. Chowdhury, K. S. Anwar et al., “Covid-19–related infodemic and its impact on public health: A global social media analysis,” Am. J. Trop. Med., vol. 103, no. 4, p. 1621, 2020.
  2. D. DiFranzo and K. Gloria-Garcia, “Filter bubbles and fake news,” XRDS, vol. 23, no. 3, p. 32–35, 2017.
  3. M. T. Bastos and D. Mercea, “The brexit botnet and user-generated hyperpartisan news,” Soc. Sci. Comput. Rev., vol. 37, no. 1, pp. 38–54, 2019.
  4. K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, “Fake news detection on social media: A data mining perspective,” SIGKDD Explor, vol. 19, no. 1, pp. 22–36, 2017.
  5. B. Guo, Y. Ding, L. Yao, Y. Liang, and Z. Yu, “The future of false information detection on social media: New perspectives and trends,” CSUR, vol. 53, no. 4, pp. 1–36, 2020.
  6. V. Pérez-Rosas, B. Kleinberg, A. Lefevre, and R. Mihalcea, “Automatic detection of fake news,” in COLING, 2018, pp. 3391–3401.
  7. K. Shu, L. Cui, S. Wang, D. Lee, and H. Liu, “dEFEND: Explainable fake news detection,” in KDD, 2019, p. 395–405.
  8. D. Rao, X. Miao, Z. Jiang, and R. Li, “STANKER: Stacking network based on level-grained attention-masked BERT for rumor detection on social media,” in EMNLP, 2021, pp. 3347–3363.
  9. J. Ma, W. Gao, and K.-F. Wong, “Rumor detection on Twitter with tree-structured recursive neural networks,” in ACL, 2018, pp. 1980–1989.
  10. T. Bian, X. Xiao, T. Xu, P. Zhao, W. Huang, Y. Rong, and J. Huang, “Rumor detection on social media with bi-directional graph convolutional networks,” in AAAI, 2020, pp. 549–556.
  11. Q. Huang, J. Yu, J. Wu, and B. Wang, “Heterogeneous graph attention networks for early detection of rumors on twitter,” in IJCNN, 2020, pp. 1–8.
  12. A. Bretto, “Hypergraph theory,” An introduction. Mathematical Engineering. Cham: Springer, 2013.
  13. Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A comprehensive survey on graph neural networks,” TNNLS, vol. 32, no. 1, pp. 4–24, 2021.
  14. S. Wu, F. Sun, W. Zhang, X. Xie, and B. Cui, “Graph neural networks in recommender systems: A survey,” ACM Comput. Surv., vol. 55, no. 5, 2022.
  15. X. Su, S. Xue, F. Liu, J. Wu, J. Yang, C. Zhou, W. Hu, C. Paris, S. Nepal, D. Jin et al., “A comprehensive survey on community detection with deep learning,” TNNLS, 2022.
  16. Y. Feng, H. You, Z. Zhang, R. Ji, and Y. Gao, “Hypergraph neural networks,” in AAAI, vol. 33, no. 01, 2019, pp. 3558–3565.
  17. N. Yadati, M. Nimishakavi, P. Yadav, V. Nitin, A. Louis, and P. Talukdar, “Hypergcn: A new method for training graph convolutional networks on hypergraphs,” in NIPS, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds., vol. 32, 2019.
  18. R. Zhang, Y. Zou, and J. Ma, “Hyper-sagnn: a self-attention based graph neural network for hypergraphs,” arXiv preprint arXiv:1911.02613, 2019.
  19. J. Jiang, Y. Wei, Y. Feng, J. Cao, and Y. Gao, “Dynamic hypergraph neural networks.” in IJCAI, 2019, pp. 2635–2641.
  20. T. Wei, Y. You, T. Chen, Y. Shen, J. He, and Z. Wang, “Augmentations in hypergraph contrastive learning: Fabricated and generative,” in NIPS, vol. 35, 2022, pp. 1909–1922.
  21. Y. Luo, “Shine: Subhypergraph inductive neural network,” in NIPS, vol. 35, 2022, pp. 18 779–18 792.
  22. J. Zhang, M. Gao, J. Yu, L. Guo, J. Li, and H. Yin, “Double-scale self-supervised hypergraph learning for group recommendation,” in CIKM, 2021, pp. 2557–2567.
  23. J. Han, Q. Tao, Y. Tang, and Y. Xia, “Dh-hgcn: Dual homogeneity hypergraph convolutional network for multiple social recommendations,” in SIGIR, 2022, pp. 2190–2194.
  24. C. Xu, M. Li, Z. Ni, Y. Zhang, and S. Chen, “Groupnet: Multiscale hypergraph neural networks for trajectory prediction with relational reasoning,” in CVPR, 2022, pp. 6498–6507.
  25. U. Jeong, K. Ding, L. Cheng, R. Guo, K. Shu, and H. Liu, “Nothing stands alone: Relational fake news detection with hypergraph neural networks,” arXiv preprint arXiv:2212.12621, 2022.
  26. K. Ding, J. Wang, J. Li, D. Li, and H. Liu, “Be more with less: Hypergraph attention networks for inductive text classification,” in EMNLP, 2020, pp. 4927–4936.
  27. R. K. Kaliyar, A. Goswami, and P. Narang, “Fakebert: Fake news detection in social media with a bert-based deep learning approach,” Multimed. Tools. Appl., vol. 80, no. 8, pp. 11 765–11 788, 2021.
  28. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in NAACL-HLT, 2019, pp. 4171–4186.
  29. X. Zhang, J. Cao, X. Li, Q. Sheng, L. Zhong, and K. Shu, “Mining dual emotion for fake news detection,” in WWW, 2021, p. 3465–3476.
  30. R. Yang, J. Ma, H. Lin, and W. Gao, “A weakly supervised propagation model for rumor verification and stance detection with multiple instance learning,” in SIGIR, 2022, p. 1761–1772.
  31. Q. Sheng, J. Cao, X. Zhang, R. Li, D. Wang, and Y. Zhu, “Zoom out and observe: News environment perception for fake news detection,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 4543–4556.
  32. B. Hu, Q. Sheng, J. Cao, Y. Zhu, D. Wang, Z. Wang, and Z. Jin, “Learn over past, evolve for future: Forecasting temporal trends for fake news detection,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, 2023, pp. 116–125.
  33. T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR, 2017.
  34. L. Yao, C. Mao, and Y. Luo, “Graph convolutional networks for text classification,” in AAAI, 2019, pp. 7370–7377.
  35. J. Wu, Q. Liu, W. Xu, and S. Wu, “Bias mitigation for evidence-aware fake news detection by causal intervention,” in SIGIR, 2022, p. 2308–2313.
  36. L. Hu, T. Yang, L. Zhang, W. Zhong, D. Tang, C. Shi, N. Duan, and M. Zhou, “Compare to the knowledge: Graph neural fake news detection with external knowledge,” in ACL/IJCNLP, 2021, pp. 754–763.
  37. Y. Ren and J. Zhang, “Fake news detection on news-oriented heterogeneous information networks through hierarchical graph attention,” in IJCNN, 2021, pp. 1–8.
  38. Y. Dou, K. Shu, C. Xia, P. S. Yu, and L. Sun, “User preference-aware fake news detection,” in SIGIR, 2021, pp. 2051–2055.
  39. X. Su, J. Yang, J. Wu, and Y. Zhang, “Mining user-aware multi-relations for fake news detection in large scale online social networks,” in WSDM, 2023, p. 51–59.
  40. E. Min and S. Ananiadou, “PESTO: A post-user fusion network for rumour detection on social media,” in Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, 2023, pp. 1–10.
  41. J. Zheng, X. Zhang, S. Guo, Q. Wang, W. Zang, and Y. Zhang, “Mfan: Multi-modal feature-enhanced attention networks for rumor detection,” in IJCAI, 2022, pp. 2413–2419.
  42. N. Mehta, M. L. Pacheco, and D. Goldwasser, “Tackling fake news detection by continually improving social context representations using graph neural networks,” in ACL, 2022, pp. 1363–1380.
  43. R. Yang, X. Wang, Y. Jin, C. Li, J. Lian, and X. Xie, “Reinforcement subgraph reasoning for fake news detection,” in KDD, 2022, p. 2253–2262.
  44. Y. Jin, X. Wang, R. Yang, Y. Sun, W. Wang, H. Liao, and X. Xie, “Towards fine-grained reasoning for fake news detection,” in AAAI, 2022, pp. 5746–5754.
  45. E. Min, Y. Rong, Y. Bian, T. Xu, P. Zhao, J. Huang, and S. Ananiadou, “Divide-and-conquer: Post-user interaction network for fake news detection on social media,” in WWW, 2022, p. 1148–1158.
  46. J. Wu and B. Hooi, “Decor: Degree-corrected social graph refinement for fake news detection,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, pp. 2582–2593.
  47. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “RoBERTa: A robustly optimized BERT pretraining approach,” arXiv preprint arXiv:1907.11692, 2019.
  48. K. Shu, D. Mahudeswaran, S. Wang, D. Lee, and H. Liu, “Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media,” Big data, vol. 8, no. 3, pp. 171–188, 2020.
  49. X. Zhou, A. Mulay, E. Ferrara, and R. Zafarani, “ReCOVery: A multimodal repository for covid-19 news credibility research,” in CIKM, 2020, pp. 3205–3212.
  50. Y. Li, B. Jiang, K. Shu, and H. Liu, “Mm-covid: A multilingual and multimodal data repository for combating covid-19 disinformation,” 2020.
  51. Y. Kim, “Convolutional neural networks for sentence classification,” in EMNLP, 2014, pp. 1746–1751.
  52. Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy, “Hierarchical attention networks for document classification,” in NAACL-HLT, 2016, pp. 1480–1489.
  53. H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale et al., “Llama 2: Open foundation and fine-tuned chat models,” arXiv preprint arXiv:2307.09288, 2023.
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Authors (4)
  1. Xing Su (14 papers)
  2. Jian Yang (505 papers)
  3. Jia Wu (93 papers)
  4. Zitai Qiu (3 papers)

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