Applying Multi-Modal Quantum Deep Learning Algorithms for Enhanced Fake News Detection
Abstract
The pervasive spread of fake news across digital platforms has prompted the development of advanced detection systems. This review surveys and compares state-of-the-art multimodal deep learning models, including SpotFake, BDANN, MVAE, EANN, and the attention-based model by Guo et al., across benchmarkdatasets such as Twitter and Weibo. We present detailed performance comparisons, with SpotFake achieving an accuracy of 86.1% on the Twitter dataset. Key contributions of this review include the introduction of taxonomy tables based on fusion strategy and model architecture, a critical comparison of early, late,and hybrid fusion mechanisms, and a comprehensive evaluation of cross-modal generalization capabilities. In addition, we explore recent efforts in Quantum Machine Learning (QML), highlighting variational quantum circuits and hybrid quantum-classical models as promising approaches for enhancing scalability and efficiency. This work serves as a roadmap for building robust, interpretable, and scalable fake news detection systems that integrate both classical and quantum techniques.References
D. Khattar, J. S. Goud, M. Gupta, and V. Varma, “Mvae: Multimodal variational autoencoder for fake news detection,” in Proceedings of the 2019 World Wide Web Conference. ACM, 2019, pp. 2915–2921. [Online]. Available: https://dl.acm.org/doi/10.1145/3308558.3313552
S. Singhal, R. R. Shah, T. Chakraborty, and P. Kumaraguru, “Spotfake: A multi-modal framework for fake news detection,” in IEEE International Conference on Multimedia Big Data (BigMM), 2019. [Online]. Available: https://www.researchgate.net/publication/337791172
Y. Wang, F. Ma, Z. Jin, Y. Yuan, G. Xun, K. Jha, L. Su, and
J. Gao, “Eann: Event adversarial neural networks for multi-modal fake
news detection,” Proceedings of the 24th ACM SIGKDD International
Conference on Knowledge Discovery & Data Mining, pp. 849–857,
[Online]. Available: https://doi.org/10.1145/3219819.3219903
I. Q. Abduljaleel and I. H. Ali, “Deep learning and fusion
mechanism-based multimodal fake news detection methodologies:
A review,” Engineering, Technology & Applied Science Research,
vol. 14, no. 4, pp. 15 665–15 675, 2024. [Online]. Available:
https://etasr.com/index.php/ETASR/article/view/7907
P. Liu, W. Qian, D. Xu, B. Ren, and J. Cao, “Multi-modal fake news
detection via bridging the gap between modals,” Entropy, vol. 25, no. 4, p. 614, 2023. [Online]. Available: https://www.mdpi.com/1099-4300/25/4/614
T. Zhang, D. Wang, H. Chen, Z. Zeng, W. Guo, C. Miao, and
L. Cui, “Bdann: Bert-based domain adaptation neural network for
multi-modal fake news detection,” in International Joint Conference
on Neural Networks (IJCNN), 2020. [Online]. Available: https:
//doi.org/10.1109/IJCNN48605.2020.9206973
Y. Guo, H. Ge, and J. Li, “A two-branch multimodal fake news
detection model based on multimodal bilinear pooling and attention
mechanism,” Frontiers in Computer Science, vol. 5, p. 1159063, 2023. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fcomp.
1159063/full
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and
S. Lloyd, “Quantum machine learning,” Nature, vol. 549, no. 7671, pp. 195–202, 2017. [Online]. Available: https://www.nature.com/articles/nature23474
M. Schuld, I. Sinayskiy, and F. Petruccione, “An introduction to
quantum machine learning,” Contemporary Physics, vol. 56, no. 2, pp.
–185, 2015. [Online]. Available: https://arxiv.org/abs/1409.3097
M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, “Circuit-centric quantum classifiers,” arXiv preprint arXiv:2008.08605, 2021. [Online].
Available: https://arxiv.org/abs/2008.08605
Z. Qu, Y. Meng, G. Muhammad, and P. Tiwari, “Qmfnd: A
quantum multimodal fusion-based fake news detection model for
social media,” Information Fusion, 2024. [Online]. Available: https:
//www.sciencedirect.com/science/article/pii/S1566253523004888
T. Bikku, S. Thota, and P. Shanmugasundaram, “A novel
quantum neural network approach to combating fake reviews,”
International Journal of Quantum Computing and Artificial Intelligence, 2024. [Online]. Available: https://link.springer.com/content/pdf/10.1007/
s44227-024-00028-x.pdf
DOI:
https://doi.org/10.31449/inf.v49i15.9053Downloads
Published
How to Cite
Issue
Section
License
I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.
I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.
In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.
I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal. The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.
Copyright © Slovenian Society Informatika
