Abstract
Fake news become a critical problem on the Internet, especially social media. During the worldwide COVID-19 epidemic, social networking sites (SNSs) are primary sources to spread false news, which are incredibly difficult to detect and regulate them since they rapidly grow everyday. With multimedia technology advances, the content of social media news now is manifested via various modalities, such as text, photos, and videos. Approaches that learn the multimodal representation for detecting fake news have evolved in recent years. Additionally, there exist diverse content domains in news platforms. Exploiting data from these domains potentially solve the data sparsity problem as well as simultaneously boosting overall performance. In this paper, we propose an effective Deep Multi-domain Multimodal Fake News Detection model for Vietnamese, v3MFND for short. Extensive experiments on a real-life dataset reveal that v3MFND improves the performance of multi-domain multimodal fake news detection for Vietnamese considerably. An ablation study is also carried out to evaluate the role of each individual modality in the multimodal model.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Mining Knowl. Manage. Process 5(2), 1 (2015)
Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Comput. 3(1), 79–87 (1991)
Khattar, D., Goud, J.S., Gupta, M., Varma, V.: MVAE: multimodal variational autoencoder for fake news detection. In: The World Wide Web Conference, pp. 2915–2921 (2019)
Kim, Y.: Convolutional neural networks for sentence classification. CoRR abs/1408.5882 (2014). https://arxiv.org/abs/1408.5882
Le, D.T., et al.: ReINTEL: a multimodal data challenge for responsible information identification on social network sites. In: Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing, pp. 84–91. Association for Computational Lingustics, Hanoi, Vietnam (2020). https://aclanthology.org/2020.vlsp-1.16
Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., Chi, E.H.: Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1930–1939 (2018)
Nan, Q., Cao, J., Zhu, Y., Wang, Y., Li, J.: Mdfend: multi-domain fake news detection. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3343–3347 (2021)
Nguyen, D.Q., Nguyen, A.T.: Phobert: pre-trained language models for vietnamese. arXiv preprint arXiv:2003.00744 (2020)
Shu, K., Wang, S., Liu, H.: 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, pp. 312–320 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Singhal, S., Kabra, A., Sharma, M., Shah, R.R., Chakraborty, T., Kumaraguru, P.: Spotfake+: a multimodal framework for fake news detection via transfer learning (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13915–13916 (2020)
Singhal, S., Shah, R.R., Chakraborty, T., Kumaraguru, P., Satoh, S.: Spotfake: a multi-modal framework for fake news detection. In: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 39–47. IEEE (2019)
Song, C., Ning, N., Zhang, Y., Wu, B.: A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks. Inf. Process. Manage. 58(1) (2021)
Tuan, N.M.D., Minh, P.Q.N.: Reintel challenge 2020: a multimodal ensemble model for detecting unreliable information on vietnamese sns. arXiv preprint arXiv:2012.10267 (2020)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)
Vu, T., Nguyen, D.Q., Nguyen, D.Q., Dras, M., Johnson, M.: Vncorenlp: a vietnamese natural language processing toolkit. arXiv preprint arXiv:1801.01331 (2018)
Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 1–40 (2016)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: Xlnet: generalized autoregressive pretraining for language understanding (2019). https://doi.org/10.48550/ARXIV.1906.08237, https://arxiv.org/abs/1906.08237
Acknowledgment
.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen Thi, CV., Vuong, TT., Le, DT., Ha, QT. (2022). v3MFND: A Deep Multi-domain Multimodal Fake News Detection Model for Vietnamese. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_49
Download citation
DOI: https://doi.org/10.1007/978-3-031-21743-2_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21742-5
Online ISBN: 978-3-031-21743-2
eBook Packages: Computer ScienceComputer Science (R0)