A fake news detection framework integrating multi-domain and multimodal features

Abstract

With the widespread dissemination of video-based fake news on social media, identifying the authenticity of information in complex contexts has become increasingly challenging. News from different domains often differs significantly in vocabulary, expression styles, and modality distributions, leading to semantic ambiguity and increasing the difficulty of cross-news modeling. To address these challenges, this paper proposes a Multimodal Multi-Domain Fake News Detection framework (MMMD), which integrates textual, audio, and visual modalities. A domain gating mechanism is introduced to model domain-specific contextual structures, thereby enhancing the discriminative power of weak modalities (such as audio) and improving inter-modal coordination. Experiments conducted on multiple benchmark datasets show that MMMD outperforms mainstream multimodal methods in terms of accuracy, F1-score, and other metrics. Notably, on the FakeSV dataset, MMMD achieves a 6.87 % improvement in accuracy over the representative method SV-FEND. Furthermore, to address the high cost of domain annotation, a K-Means-based pseudo-label generation strategy is adopted. Comparative experiments across different numbers of clusters indicate that setting 10 yields performance close to that of human annotations, validating the method’s feasibility in low-supervision scenarios. Without relying on external user relationships, MMMD leverages domain-aware semantic structures and modality interaction mechanisms, providing an efficient and scalable solution for multimodal fake news detection in complex environments.