With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society. One of the unique challenges for fake news detection on social media is how to detect fake news on future events. Recently, numerous fake news detection models that utilize textual information and the propagation structure of posts have been proposed. Unfortunately, most of the existing approaches can hardly handle this challenge since they rely heavily on event-specific features for prediction and cannot generalize to unseen events. To address this, we introduce \textbf{F}uture \textbf{AD}aptive \textbf{E}vent-based Fake news Detection (FADE) framework. Specifically, we train a target predictor through an adaptive augmentation strategy and graph contrastive learning to obtain higher-quality features and make more accurate overall predictions. Simultaneously, we independently train an event-only predictor to obtain biased predictions. We further mitigate event bias by subtracting the event-only predictor's output from the target predictor's output to obtain the final prediction. Encouraging results from experiments designed to emulate real-world social media conditions validate the effectiveness of our method in comparison to existing state-of-the-art approaches.

翻译：随着社交媒体的快速发展，虚假新闻在社交媒体上的广泛传播日益威胁着个人与社会。社交媒体虚假新闻检测面临的一个独特挑战是如何检测未来事件的虚假新闻。近年来，已提出众多利用文本信息和帖子传播结构的虚假新闻检测模型。遗憾的是，现有方法大多难以应对这一挑战，因为它们严重依赖事件特定特征进行预测，无法泛化到未见事件。为解决此问题，我们提出了**F**uture **AD**aptive **E**vent-based Fake news Detection (FADE) 框架。具体而言，我们通过自适应增强策略和图对比学习训练一个目标预测器，以获得更高质量的特征并做出更准确的总体预测。同时，我们独立训练一个仅事件预测器以获得有偏预测。我们通过从目标预测器的输出中减去仅事件预测器的输出以缓解事件偏差，从而获得最终预测。为模拟真实社交媒体条件而设计的实验所取得的鼓舞性结果，验证了我们的方法相较于现有最先进方法的有效性。