Abstract
Fake news is one of the most prominent forms of disinformation. Unfortunately, today’s advanced social media platforms allow for the rapid transmission of fake news, which may negatively impact several aspects of human society. Despite the significant progress in detecting fake news, the focus of most current work lies in the detection based on content-based or user context-based methods. We believe that such methods suffer from two limitations: the lack of characterizing the news variation (fake news can appear in different forms via tweets, such as writing different tweets about the same news article) and news repetition (fake news is shared repeatedly via retweets); and the absence of the temporal engagement among different social interactions. Thus, we propose a novel detection framework, namely the Temporal graph Fake News Detection Framework (T-FND), that is effectively able to capture heterogeneous and repetitive characteristics of fake news behavior, resulting in better prediction performance. We empirically evaluate the effectiveness of our model on two real-world datasets, showing that our solution outperforms the state-of-the-art baseline methods
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)
Dong, L., et al.: Unified language model pre-training for natural language understanding and generation. In: NIPS (2019)
Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems (2017)
Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP, pp. 1746–1751 (2014)
Liao, H., Liu, Q., Shu, K., et al.: Fake news detection through graph comment advanced learning. arXiv preprint arXiv:2011.01579 (2020)
Ravazzi, C., Tempo, R., Dabbene, F.: Learning influence structure in sparse social networks. IEEE Transactions on Control of Network Systems (2017)
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., Liu, Y.: Combating fake news: A survey on identification and mitigation techniques. ACM Transactions on Intelligent Systems and Technology (TIST) (2019)
Shu, K., Cui, L., Wang, S., Lee, D., Liu, H.: dEFEND: explainable fake news detection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: a data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big data (2020)
Song, C., Shu, K., Wu, B.: Temporally evolving graph neural network for fake news detection. Inf. Process. Manag. 58, 102712 (2021)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2016)
Yun, S., Jeong, M., Kim, R., Kang, J., Kim, H.J.: Graph transformer networks. In: NIPS (2019)
Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)
Acknowledgment
This work was supported in part by the National Science Foundation Program under award No. 1939725.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Alharbi, R., Jeter, T.R., Thai, M.T. (2023). Detection of Fake News Through Heterogeneous Graph Interactions. In: Mohaisen, D., Wies, T. (eds) Networked Systems. NETYS 2023. Lecture Notes in Computer Science, vol 14067. Springer, Cham. https://doi.org/10.1007/978-3-031-37765-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-37765-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-37764-8
Online ISBN: 978-3-031-37765-5
eBook Packages: Computer ScienceComputer Science (R0)