Open Access
| Issue |
MATEC Web Conf.
Volume 395, 2024
2023 2nd International Conference on Physics, Computing and Mathematical (ICPCM2023)
|
|
|---|---|---|
| Article Number | 01013 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/matecconf/202439501013 | |
| Published online | 15 May 2024 | |
- Shu K., Sliva A., Wang S., et al. Fake news detection on social media: a data mining perspective [J]. ACM SIGKDD Explorations Newsletter, 2017, 19(1): 22-36 [CrossRef] [Google Scholar]
- Wu K., Yang S., Zhu K. Q. False rumors detection on Sinaweibo by propagation structures [C]// 2015 IEEE 31st International Conference on Data Engineering, Seoul, Korea, 2015: 651-662 [CrossRef] [Google Scholar]
- Gupta M., Zhao P., Han J. Evaluating event credibility on twitter[C] “ Proceedings of the 2012 SIAM International Conference on Data Mining, Anaheim, USA, 2012: 153-164 [CrossRef] [Google Scholar]
- Dhruv Khattar, Jaipal Singh Goud, Manish Gupta, and Vasudeva Varma. 2019. MVAE: Multimodal variational autoencoder for fake news detection. In The World Wide Web Conference. 2915-2921 [CrossRef] [Google Scholar]
- Jin Z., Cao J., Guo H., et al. Multimodal fusion with recurrent neural networks for rumor detection on microblogs [C] “Proceedings of the 25th ACM International Conference on Multimedia, Mountain View, USA, 2017: 795-816 [CrossRef] [Google Scholar]
- Wang Y., Ma F., Jin Z., et al. Eann: event adversarial neural networks for multimodal fake news detection[C] “ Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, 2018: 849-857 [CrossRef] [Google Scholar]
- Qian S., Wang J., Hu J., et al. Hierarchical multi-modal contextual attention network for fake news detection [C]//Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 2021: 153-162. [Google Scholar]
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014). [Google Scholar]
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. 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 2019.4171-4186. [Google Scholar]
- Kim W., Son B., Kim I. ViLT: vision-and-language transformer without convolution or region supervision [C]//Proceedings of the 38th International Conference on Machine Learning, Jul 18-24, 2021: 5583-5594. [Google Scholar]
- Nagrani A., Yang S., Arnab A., et al. Attention bottlenecks for multimodal fusion [C]//Advances in Neural Information Processing Systems 34, Dec 6-14, 2021: 14200-14213. [Google Scholar]
- Song C., Shu K., Wu B. Temporally evolving graph neural network for fake news detection[J]. Information Processing & Management, 2021, 58(6):102712. [CrossRef] [Google Scholar]
- Cui J., Kim K., Na S. H., et al. Meta-path-based fake news detection leveraging multilevel social context information [C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, Oct 17-21, 2022. New York: ACM, 2022: 325-334. [Google Scholar]
- Zhiwei Jin, Juan Cao, Han Guo, Yongdong Zhang, and Jiebo Luo. 2017. Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In Proceedings of the 25th ACM international conference on Multimedia. ACM, 795-816. [Google Scholar]
- Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic differentiation in pytorch. (2017). [Google Scholar]
- Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). [Google Scholar]
- Xinyi Zhou, Jindi Wu, and R. Zafarani. 2020. SAFE: Similarity-Aware Multi-Modal Fake News Detection. ArXivabs/2003.04981 (2020). [Google Scholar]
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