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 benchmark
datasets 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.
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DOI: https://doi.org/10.31449/inf.v49i15.9053
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