MyGO: Modality-incomplete Fake News Video Detection via Prompt-assisted Modality Disentangling Model

Fake news video detection has become a pressing concern with the growth of short video platforms. However, previous studies have primarily focused on videos with modality-complete data, failing to handle the uncertain missing modality issue in real-world applications. They fall short in two key aspects: (1) Highly coupled feature fusion hinders the model to learn intra- and inter-modality dependencies, making it difficult to form robust multimodal representations when facing uncertain modality missing. (2) Excessive reliance on discriminative modality combinations toward fake news, which leads to inferior performance on other modality combinations. To this end, we propose a novel model for modality-incomplete fake news video detection called MyGO. It contains three modules: (1) Caption-guided Keyframe Attention (CKA) leverages embedded captions to guide feature extraction, which adaptively excludes irrelevant frames to enhance the learning of intra-modality dependencies, resulting in refined modality features. (2) Based on refined modality features from CKA, Modality Disentangling Network (MDN) is designed to decompose them into shared and specific parts, which captures fine-grained inter-modality dependencies effectively. These two kinds of dependencies help avoid coupled multimodal fusion and bridge information gaps caused by missing modalities. (3) Furthermore, missing prompts are newly introduced to explicitly mark modality combinations within each news video. By integrating missing prompts with aforementioned inter-modality dependencies within Prompt-assisted Modality Aligning (PMA) Module, we alleviate over-reliance on discriminative modality combinations and enhancing the representation of less discriminative ones. Extensive experiments showcase that MyGO achieves 3.79–4.85% improvements in accuracy, demonstrating its performance over state-of-the-art approaches under different missing conditions.