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Original Article
Multimodal Fake News Detection Using Deep Learning Methods
Vishal Tiwari1
Preeti Panjwani2
Reenit Shelare3
Nibodh Shide4
Sarthak Raut5
12345 Department of Information Technology, St. Vincent Pallotti College of Engineering and Technology, Nagpur, Maharashtra, India.
Published Online: March-April 2025
Pages: 165-169
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250602022References
1. H. Shu, A. Sliva, S. Wang, J. Tang and H. Liu, "Fake News Detection on Social Media: A Data Mining Perspective," ACM SIGKDD
Explorations Newsletter, vol. 19, no. 1, pp. 22–36, 2017.
2. K. Zhou, R. Zafarani, "A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities," ACM Computing
Surveys (CSUR), vol. 55, no. 5, pp. 1–41, 2023.
3. S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
4. A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," in Advances
in Neural Information Processing Systems, vol. 25, pp. 1097–1105, 2012.
5. M. Khattar, J. Goud, M. Gupta and V. Varma, "MDEA: Multimodal Dual-Emotion Attention for Fake News Detection," in Proc. of the
28th ACM International Conference on Information and Knowledge Management (CIKM), pp. 2821–2829, 2019.
6. T. J. Wang, A. Shankar and W. Y. Wang, "Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection,"
in Proc. of the 12th Language Resources and Evaluation Conference (LREC), pp. 6072–6080, 2020.
7. Y. Freund and R. E. Schapire, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," Journal
of Computer and System Sciences, vol. 55, no. 1, pp. 119–139, 1997.
8. Vaswani et al., "Attention is All You Need," in Advances in Neural Information Processing Systems, vol. 30, pp. 5998–6008, 2017.
9. [Z. Liu et al., "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows," in Proc. of the IEEE/CVF International
Conference on Computer Vision (ICCV), pp. 10012–10022, 2021.
10. A. Dosovitskiy et al., "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale," in Proc. of the 9th International
Conference on Learning Representations (ICLR), 2021.
11. Z. Jin, J. Cao, Y. Zhang, Y. Zhou and Q. Tian, "Novel Visual and Statistical Image Features for Microblogs News Verification," IEEE
Transactions on Multimedia, vol. 19, no. 3, pp. 598–608, Mar. 2017, doi: 10.1109/TMM.2016.2618295.
12. S. Singhania, N. Fernandez and S. Rao, "3HAN: A Deep Neural Network for Fake News Detection," in Proc. of the 31st ACM
Conference on Hypertext and Social Media (HT'20), pp. 203–207, 2020.
13. D. Roy, S. S. Saha, D. Ghosh and S. Chakraborty, "Fake News Detection Using Deep Learning Based Multimodal Fusion," in Proc.
of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1337–1340, 2019.
14. J. Qian, M. Gong, Y. Liu and L. Liu, "Adversarial Fake News Detection on Multimodal Social Media," in Proc. of the 34th AAAI
Conference on Artificial Intelligence, vol. 34, no. 1, pp. 86–93, 2020.
15. R. Agarwal and A. Sureka, "Transformer for Detecting Fake News Using Multimodal Content," in Proc. of the 18th International
Conference on Natural Language Processing (ICON), pp. 118–127, 2021
Explorations Newsletter, vol. 19, no. 1, pp. 22–36, 2017.
2. K. Zhou, R. Zafarani, "A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities," ACM Computing
Surveys (CSUR), vol. 55, no. 5, pp. 1–41, 2023.
3. S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
4. A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," in Advances
in Neural Information Processing Systems, vol. 25, pp. 1097–1105, 2012.
5. M. Khattar, J. Goud, M. Gupta and V. Varma, "MDEA: Multimodal Dual-Emotion Attention for Fake News Detection," in Proc. of the
28th ACM International Conference on Information and Knowledge Management (CIKM), pp. 2821–2829, 2019.
6. T. J. Wang, A. Shankar and W. Y. Wang, "Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection,"
in Proc. of the 12th Language Resources and Evaluation Conference (LREC), pp. 6072–6080, 2020.
7. Y. Freund and R. E. Schapire, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," Journal
of Computer and System Sciences, vol. 55, no. 1, pp. 119–139, 1997.
8. Vaswani et al., "Attention is All You Need," in Advances in Neural Information Processing Systems, vol. 30, pp. 5998–6008, 2017.
9. [Z. Liu et al., "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows," in Proc. of the IEEE/CVF International
Conference on Computer Vision (ICCV), pp. 10012–10022, 2021.
10. A. Dosovitskiy et al., "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale," in Proc. of the 9th International
Conference on Learning Representations (ICLR), 2021.
11. Z. Jin, J. Cao, Y. Zhang, Y. Zhou and Q. Tian, "Novel Visual and Statistical Image Features for Microblogs News Verification," IEEE
Transactions on Multimedia, vol. 19, no. 3, pp. 598–608, Mar. 2017, doi: 10.1109/TMM.2016.2618295.
12. S. Singhania, N. Fernandez and S. Rao, "3HAN: A Deep Neural Network for Fake News Detection," in Proc. of the 31st ACM
Conference on Hypertext and Social Media (HT'20), pp. 203–207, 2020.
13. D. Roy, S. S. Saha, D. Ghosh and S. Chakraborty, "Fake News Detection Using Deep Learning Based Multimodal Fusion," in Proc.
of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1337–1340, 2019.
14. J. Qian, M. Gong, Y. Liu and L. Liu, "Adversarial Fake News Detection on Multimodal Social Media," in Proc. of the 34th AAAI
Conference on Artificial Intelligence, vol. 34, no. 1, pp. 86–93, 2020.
15. R. Agarwal and A. Sureka, "Transformer for Detecting Fake News Using Multimodal Content," in Proc. of the 18th International
Conference on Natural Language Processing (ICON), pp. 118–127, 2021
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