用生成弹幕模拟真实互动，提升假新闻检测精度
Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku
- 构建生成式弹幕框架，模拟用户反应时间与情绪表达
- 在中英文数据集上均超越现有模型，准确率显著提升
- 适合关注社交互动与多模态融合的假新闻研究者
现代多媒体平台上的弹幕（Danmaku）互动能反映观点冲突与共识，蕴含细粒度的社会信号，有助于假新闻检测。然而，弹幕固有的延迟特性违背了假新闻检测的实时需求，导致相关研究匮乏。为此，本文提出新型时序生成弹幕框架Genda，包含弹幕触发器（预测用户反应的时间与强度）和弹幕生成器（合成语义与情感表达），共同构建时序对齐且类人的伪弹幕流。进一步设计了弹幕引导的时序多模态假新闻检测模型DM-FEND，实现视频、音频、文本与弹幕间的细粒度交互，增强动态模态对齐并抑制语义噪声。实验表明，DM-FEND在中文（FakeSV）和英文（FakeTT）基准上均持续优于主流基线。消融实验验证了时序弹幕建模对鲁棒性与判别能力的关键作用。本研究为应对新闻与用户行为间的时间不一致问题，提供了有效且稳健的多模态假新闻检测方案。
原文摘要 · Abstract (English)
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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