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HyperGraphDis: Leveraging Hypergraphs for Contextual and Social-Based Disinformation Detection (2310.01113v4)

Published 2 Oct 2023 in cs.SI

Abstract: In light of the growing impact of disinformation on social, economic, and political landscapes, accurate and efficient identification methods are increasingly critical. This paper introduces HyperGraphDis, a novel approach for detecting disinformation on Twitter that employs a hypergraph-based representation to capture (i) the intricate social structures arising from retweet cascades, (ii) relational features among users, and (iii) semantic and topical nuances. Evaluated on four Twitter datasets -- focusing on the 2016 U.S. Presidential election and the COVID-19 pandemic -- HyperGraphDis outperforms existing methods in both accuracy and computational efficiency, underscoring its effectiveness and scalability for tackling the challenges posed by disinformation dissemination. HyperGraphDis displays exceptional performance on a COVID-19-related dataset, achieving an impressive F1 score (weighted) of approximately 89.5%. This result represents a notable improvement of around 4% compared to the other state-of-the-art methods. Additionally, significant enhancements in computation time are observed for both model training and inference. In terms of model training, completion times are accelerated by a factor ranging from 2.3 to 7.6 compared to the second-best method across the four datasets. Similarly, during inference, computation times are 1.3 to 6.8 times faster than the state-of-the-art.

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References (49)
  1. Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In Traore, I.; Woungang, I.; and Awad, A., eds., Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, 127–138. Cham: Springer International Publishing. ISBN 978-3-319-69155-8.
  2. Hypergraph convolution and hypergraph attention. Pattern Recognition, 110: 107637.
  3. A Data-driven Understanding of Left-Wing Extremists on Social Media. arXiv:2307.06981.
  4. Fake News as a Threat to National Security. International conference KNOWLEDGE-BASED ORGANIZATION, 24: 19–22.
  5. Bisseling, R. 2020. Graph matching, 291–358. ISBN 9780198788348.
  6. A Measure of Similarity between Graph Vertices: Applications to Synonym Extraction and Web Searching. SIAM Review, 46: 647–666.
  7. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10): P10008.
  8. Information Credibility on Twitter. In Proceedings of the 20th International Conference on World Wide Web, WWW ’11, 675–684. New York, NY, USA: Association for Computing Machinery. ISBN 9781450306324.
  9. Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues. arXiv:2211.05289.
  10. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’19, 257–266. New York, NY, USA: Association for Computing Machinery. ISBN 9781450362016.
  11. Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository. Proceedings of the International AAAI Conference on Web and Social Media, 14(1): 853–862.
  12. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT.
  13. SOMPS-Net: Attention Based Social Graph Framework for Early Detection of Fake Health News. In Xu, Y.; Wang, R.; Lord, A.; Boo, Y. L.; Nayak, R.; Zhao, Y.; and Williams, G., eds., Data Mining, 165–179. Singapore: Springer Singapore. ISBN 978-981-16-8531-6.
  14. Misleading Repurposing on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 17: 209–220.
  15. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.
  16. Towards automatic fake news classification. Proceedings of the Association for Information Science and Technology, 55: 805–807.
  17. Article: A Survey of Text Similarity Approaches. International Journal of Computer Applications, 68(13): 13–18. Full text available.
  18. Rumor Detection with Hierarchical Social Attention Network. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM ’18, 943–951. New York, NY, USA: Association for Computing Machinery. ISBN 9781450360142.
  19. Evaluating Event Credibility on Twitter. Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012, 153–164.
  20. Sub-Standards and Mal-Practices: Misinformation’s Role in Insular, Polarized, and Toxic Interactions. arXiv:2301.11486.
  21. A Golden Age: Conspiracy Theories’ Relationship with Misinformation Outlets, News Media, and the Wider Internet. arXiv:2301.10880.
  22. Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks. In 2022 IEEE International Conference on Big Data (Big Data), 596–605. Los Alamitos, CA, USA: IEEE Computer Society.
  23. Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs. 795–816.
  24. FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimedia Tools and Application.
  25. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on scientific Computing, 20(1): 359–392.
  26. The Menlo report: Ethical principles guiding information and communication technology research. Available at SSRN 2445102.
  27. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations, ICLR ’17.
  28. A Meta-Analysis of State-of-the-Art Automated Fake News Detection Methods. IEEE Transactions on Computational Social Systems, 1–11.
  29. Toward A Multilingual and Multimodal Data Repository for COVID-19 Disinformation. In 2020 IEEE International Conference on Big Data (Big Data), 4325–4330.
  30. Distributed Algorithms for Topic Models. Journal of Machine Learning Research, 10(62): 1801–1828.
  31. A Unified Graph-Based Approach to Disinformation Detection Using Contextual and Semantic Relations. Proceedings of the International AAAI Conference on Web and Social Media, 16: 747–758.
  32. DeepWalk: Online Learning of Social Representations. In Proceedings of the 20th International Conference on Knowledge Discovery and Data Mining, KDD ’14, 701–710. ACM.
  33. Swinging in the States: Does disinformation on Twitter mirror the US presidential election system? 1395–1403.
  34. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4): 1118–1123.
  35. Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20), 3125–3132. ACM.
  36. Did State-Sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter. In Arief, B.; Monreale, A.; Sirivianos, M.; and Li, S., eds., Security and Privacy in Social Networks and Big Data, 58–76. Singapore: Springer Nature Singapore. ISBN 978-981-99-5177-2.
  37. Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences, 106(16): 6483–6488.
  38. DEFEND: Explainable Fake News Detection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’19, 395–405. New York, NY, USA: Association for Computing Machinery. ISBN 9781450362016.
  39. Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation. Proceedings of the International AAAI Conference on Web and Social Media, 14(1): 626–637.
  40. Beyond News Contents: The Role of Social Context for Fake News Detection. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM ’19, 312–320. New York, NY, USA: Association for Computing Machinery. ISBN 9781450359405.
  41. The Role of User Profiles for Fake News Detection. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’19, 436–439. New York, NY, USA: Association for Computing Machinery. ISBN 9781450368681.
  42. Some Like it Hoax: Automated Fake News Detection in Social Networks. arXiv:1704.07506.
  43. Sharing clusters among related groups: Hierarchical Dirichlet processes. NIPS’05, 1385–1392. Advances in Neural Information Processing Systems.
  44. Social Media as Information Source: Recency of Updates and Credibility of Information. J. Comp.-Med. Commun., 19(2): 171–183.
  45. Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM ’18, 637–645. New York, NY, USA: Association for Computing Machinery. ISBN 9781450355810.
  46. Graph similarity scoring and matching. Applied Mathematics Letters, 21: 86–94.
  47. Character-level Convolutional Networks for Text Classification. In Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc.
  48. A Novel Method for Graph Matching. 177–181.
  49. A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities. ACM Computing Surveys, 53.
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