提出更真实的假新闻检测评估方式，提升模型在真实场景下的表现。
Revisiting Fake News Detection: Towards Temporality-aware Evaluation by Leveraging Engagement Earliness
- 构建时间感知的评估框架，训练仅使用历史数据
- 发现后期互动信息会引入虚假关联，降低检测精度
- 利用互动早期性特征抑制噪声边，适合真实社交环境
基于社交图谱的假新闻检测通过用户信息、推文和评论等社交上下文识别虚假信息。然而，传统方法在评估时往往使用未来数据，不符合真实场景。本文提出一种更具现实意义的评估方案：模型只能基于截至某一时间点的历史数据进行训练。实验表明，传统方法在此新设置下性能显著下降。为此，我们提出DAWN方法，利用互动早期性特征来识别并削弱社交图中连接真实与虚假新闻的噪声边，从而提升检测效果。结果表明，在真实场景下，该方法优于现有主流方法。代码已开源：https://github.com/LeeJunmo/DAWN。
原文摘要 · Abstract (English)
Social graph-based fake news detection aims to identify news articles containing false information by utilizing social contexts, e.g., user information, tweets and comments. However, conventional methods are evaluated under less realistic scenarios, where the model has access to future knowledge on article-related and context-related data during training. In this work, we newly formalize a more realistic evaluation scheme that mimics real-world scenarios, where the data is temporality-aware and the detection model can only be trained on data collected up to a certain point in time. We show that the discriminative capabilities of conventional methods decrease sharply under this new setting, and further propose DAWN, a method more applicable to such scenarios. Our empirical findings indicate that later engagements (e.g., consuming or reposting news) contribute more to noisy edges that link real news-fake news pairs in the social graph. Motivated by this, we utilize feature representations of engagement earliness to guide an edge weight estimator to suppress the weights of such noisy edges, thereby enhancing the detection performance of DAWN. Through extensive experiments, we demonstrate that DAWN outperforms existing fake news detection methods under real-world environments. The source code is available at https://github.com/LeeJunmo/DAWN.
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