Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku

The social interactions among crowds via textitDanmaku (a.k.a., bullet comments) on modern multimedia platforms can facilitate both viewpoint conflicts and consensus, providing fine-grained discriminative social signals that can benefit fake news detection. However, the inherent accumulation latency of textitDanmaku in real-world scenarios violates the real-time necessity of fake news detection, making the studies of textitDanmaku-related fake news detection underexplored. To break this violation, we simulate this temporal-aware user interactive process by proposing a novel temporal Generative danmaku framework, called Genda, which consists of: (1) a textitDanmaku Trigger for predicting the timing and intensity of user reactions; and (2) a textitDanmaku Generator for synthesizing corresponding semantic and emotional expressions, thereby mutually constructing a temporally aligned and human-like pseudo textitDanmaku streams. To make the generated textitDanmaku useful for identifying fake news videos, we further design a textitDanmaku-guided Temporal Multimodal fake news detection model - DM-FEND, which enables fine-grained multimodal interactions among video, audio, text, and textitDanmaku, enhancing dynamic modalities alignment and semantic noise inhibition. The experimental results demonstrate that DM-FEND consistently outperforms state-of-the-art baselines across both Chinese (FakeSV) and English (FakeTT) benchmarks. Further ablations validate the crucial role of temporal textitDanmaku modeling in enhancing robustness and discriminative capability. Finally, this study offers a bright and robust solution for multimodal fake news detection in modern social interactive fashions by bridging the temporal inconsistency between news and user behaviors.