arXiv:2510.05839v1 Announce Type: new
Abstract: Multimodal fake news detection (MFND) has become an urgent task with the emergence of huge multimodal fake content on social media platforms. Previous studies mainly focus on complex feature extraction and fusion to learn discriminative information from multimodal content. However, in real-world applications, multimedia news may naturally lose some information during dissemination, resulting in modality incompleteness, which is detrimental to the generalization and robustness of existing models. To this end, we propose a novel generic and robust multimodal fusion strategy, termed Multi-expert Modality-incomplete Learning Network (MMLNet), which is simple yet effective. It consists of three key steps: (1) Multi-Expert Collaborative Reasoning to compensate for missing modalities by dynamically leveraging complementary information through multiple experts. (2) Incomplete Modality Adapters compensates for the missing information by leveraging the new feature distribution. (3) Modality Missing Learning leveraging an label-aware adaptive weighting strategy to learn a robust representation with contrastive learning. We evaluate MMLNet on three real-world benchmarks across two languages, demonstrating superior performance compared to state-of-the-art methods while maintaining relative simplicity. By ensuring the accuracy of fake news detection in incomplete modality scenarios caused by information propagation, MMLNet effectively curbs the spread of malicious misinformation. Code is publicly available at https://github.com/zhyhome/MMLNet.
Expert Commentary on Multimodal Fake News Detection
Fake news detection in the era of social media has become an increasingly important and challenging task. With the rise of multimodal content, traditional methods focusing solely on text analysis are no longer sufficient. This article highlights the significance of multimodal fake news detection (MFND) and addresses the issue of modality incompleteness that can affect the accuracy of existing models.
Multi-disciplinary Concepts
The concepts discussed in this article encompass various disciplines such as artificial intelligence, computer vision, natural language processing, and information theory. The fusion of different modalities (text, image, video) requires a multi-disciplinary approach that combines methods from different fields to effectively detect fake news.
Related Fields
Multi-expert Modality-incomplete Learning Network (MMLNet) incorporates concepts and techniques related to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By leveraging multiple experts for collaborative reasoning and adapting to incomplete modalities, MMLNet showcases the integration of different fields to enhance fake news detection.
Future Directions
The proposed MMLNet provides a strong foundation for improving the robustness and generalization of fake news detection models in real-world applications. Future research in this area could explore the development of even more sophisticated fusion strategies and adaptive learning techniques to address evolving challenges in misinformation dissemination.
Overall, this article sheds light on the complexities of detecting fake news in a multimodal environment and underscores the importance of multi-disciplinary approaches for effective solutions.