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

• URL: http://arxiv.org/abs/2608.22832v1
• Date: Mon, 24 Aug 2026 05:50:12 GMT
• Title: Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku
• Abstract summary: textitDanmaku (a.k.a., bullet comments) provides fine-grained discriminative social signals that can benefit fake news detection.<n>The inherent accumulation latency of textitDanmaku in real-world scenarios violates the real-time necessity of fake news detection.<n>We propose a temporal-aware user interactive process by proposing a novel temporal textbfGenerative textbfdanmaku framework.
• Score: 11.425128043674958
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The social interactions among crowds via \textit{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 \textit{Danmaku} in real-world scenarios violates the real-time necessity of fake news detection, making the studies of \textit{Danmaku}-related fake news detection underexplored. To break this violation, we simulate this temporal-aware user interactive process by proposing a novel temporal \textbf{Gen}erative \textbf{da}nmaku framework, called \textbf{Genda}, which consists of: (1) a \textit{Danmaku} Trigger for predicting the timing and intensity of user reactions; and (2) a \textit{Danmaku} Generator for synthesizing corresponding semantic and emotional expressions, thereby mutually constructing a temporally aligned and human-like pseudo \textit{Danmaku} streams. To make the generated \textit{Danmaku} useful for identifying fake news videos, we further design a \textit{Danmaku}-guided Temporal Multimodal fake news detection model - \textbf{DM-FEND}, which enables fine-grained multimodal interactions among video, audio, text, and \textit{Danmaku}, enhancing dynamic modalities alignment and semantic noise inhibition. The experimental results demonstrate that \emph{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 \textit{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.

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