解决新类型假新闻检测难题，通过去偏提升跨领域泛化能力
Unseen Fake News Detection Through Casual Debiasing
- 基于因果分析设计重加权策略，缓解特定领域数据偏差
- 在非重叠新闻领域数据集上显著提升未见假新闻识别率
- 适合关注跨域泛化与真实场景假新闻检测的研究者
社交媒体上假新闻的广泛传播带来重大风险，亟需及时准确的检测。然而，现有方法因依赖历史事件和领域的训练数据，难以应对新型假新闻。本文通过因果分析识别训练数据中的领域特异性偏差，提出名为 FNDCD 的去偏方法。该方法结合分类置信度与传播结构正则化的重加权策略，降低领域偏差影响，增强对未见假新闻的检测能力。在真实世界、非重叠新闻领域的数据集上实验表明，FNDCD 显著提升了跨领域泛化性能。
原文摘要 · Abstract (English)
The widespread dissemination of fake news on social media poses significant risks, necessitating timely and accurate detection. However, existing methods struggle with unseen news due to their reliance on training data from past events and domains, leaving the challenge of detecting novel fake news largely unresolved. To address this, we identify biases in training data tied to specific domains and propose a debiasing solution FNDCD. Originating from causal analysis, FNDCD employs a reweighting strategy based on classification confidence and propagation structure regularization to reduce the influence of domain-specific biases, enhancing the detection of unseen fake news. Experiments on real-world datasets with non-overlapping news domains demonstrate FNDCD's effectiveness in improving generalization across domains.
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