GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking (2312.05739v1)
Abstract: With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability. Although deep learning methods like CNNs, RNNs, and Transformer-based models like BERT have enhanced fake news detection, they primarily focus on content, overlooking social context during news propagation. Graph-based techniques have incorporated this social context but are limited by the need for large labeled datasets. Addressing these challenges, this paper introduces GAMC, an unsupervised fake news detection technique using the Graph Autoencoder with Masking and Contrastive learning. By leveraging both the context and content of news propagation as self-supervised signals, our method negates the requirement for labeled datasets. We augment the original news propagation graph, encode these with a graph encoder, and employ a graph decoder for reconstruction. A unique composite loss function, including reconstruction error and contrast loss, is designed. The method's contributions are: introducing self-supervised learning to fake news detection, proposing a graph autoencoder integrating two distinct losses, and validating our approach's efficacy through real-world dataset experiments.
- Rumor detection on social media with bi-directional graph convolutional networks. In Proceedings of the AAAI conference on artificial intelligence, volume 34, 549–556.
- 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, Volume 1 (Long and Short Papers), 4171–4186.
- User Preference-Aware Fake News Detection. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2051–2055.
- Unsupervised Rumor Detection Based on Propagation Tree VAE. IEEE Transactions on Knowledge and Data Engineering, 1–16.
- Unsupervised Fake News Detection: A Graph-Based Approach. In Proceedings of the 31st ACM Conference on Hypertext and Social Media, 75–83.
- Rumor Detection on Social Media with Event Augmentations. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020–2024.
- GraphMAE: Self-Supervised Masked Graph Autoencoders. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 594–604.
- Variational graph auto-encoders. arXiv preprint arXiv:1611.07308.
- Unsupervised Fake News Detection Based on Autoencoder. IEEE Access, 9: 29356–29365.
- A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems.
- Detecting Rumors from Microblogs with Recurrent Neural Networks. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 3818–3824.
- Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media. In Proceedings of the ACM Web Conference 2022, 1148–1158.
- FakeNewsNet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data, 8(3): 171–188.
- Fake News Detection on Social Media: A Data Mining Perspective. SIGKDD Explor. Newsl., 19(1): 22–36.
- Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals. arXiv:2305.11349.
- Rumor Detection on Social Media with Graph Adversarial Contrastive Learning. In Proceedings of the ACM Web Conference 2022, 2789–2797.
- EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 849–857.
- Fake News Detection via Knowledge-Driven Multimodal Graph Convolutional Networks. In Proceedings of the 2020 International Conference on Multimedia Retrieval, 540–547.
- Lightweight Source Localization for Large-Scale Social Networks. In Proceedings of the ACM Web Conference 2023, 286–294.
- Self-supervised learning on graphs: Contrastive, generative, or predictive. IEEE Transactions on Knowledge and Data Engineering.
- Category-Controlled Encoder-Decoder for Fake News Detection. IEEE Transactions on Knowledge and Data Engineering, 35(2): 1242–1257.
- How Powerful are Graph Neural Networks? In International Conference on Learning Representations.
- Unsupervised Fake News Detection on Social Media: A Generative Approach. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, 5644–5651.
- Truth Discovery with Multiple Conflicting Information Providers on the Web. IEEE Transactions on Knowledge and Data Engineering, 20(6): 796–808.
- Graphrnn: Generating realistic graphs with deep auto-regressive models. In International conference on machine learning, 5708–5717.
- Jointly embedding the local and global relations of heterogeneous graph for rumor detection. In 2019 IEEE international conference on data mining (ICDM), 796–805. IEEE.
- SAFE: Similarity-Aware Multi-modal Fake News Detection. In Pacific-Asia Conference on knowledge discovery and data mining, 354–367.
- A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities. ACM Comput. Surv., 53(5).