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

arXiv:2608.22832v1 Announce Type: new Abstract: The social interactions among crowds via extit{Danmaku} (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 extit{Danmaku} in real-world scenarios violates the real-time necessity of fake news detection, making the studies of extit{Danmaku}-related fake news detection underexplored. To break this violation, we simulate this temporal-aware user interactive process by proposing a novel temporal extbf{Gen}erative extbf{da}nmaku framework, called extbf{Genda}, which consists of: (1) a extit{Danmaku} Trigger for predicting the timing and intensity of user reactions; and (2) a extit{Danmaku} Generator for synthesizing corresponding semantic and emotional expressions, thereby mutually constructing a temporally aligned and human-like pseudo extit{Danmaku} streams. To make the generated extit{Danmaku} useful for identifying fake news videos, we further design a extit{Danmaku}-guided Temporal Multimodal fake news detection model - extbf{DM-FEND}, which enables fine-grained multimodal interactions among video, audio, text, and extit{Danmaku}, 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 extit{Danmaku} 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.

Source: arXiv cs.AI | 2026-08-25