Unseen Fake News Detection Through Casual Debiasing

• URL: http://arxiv.org/abs/2503.04160v1
• Date: Thu, 06 Mar 2025 07:23:44 GMT
• Title: Unseen Fake News Detection Through Casual Debiasing
• Authors: Shuzhi Gong, Richard Sinnott, Jianzhong Qi, Cecile Paris,
• Abstract summary: Existing methods struggle with unseen news due to their reliance on training data from past events and domains.<n>We propose a debiasing solution FNDCD, which employs a reweighting strategy based on classification confidence and propagation structure regularization.<n>Experiments on real-world datasets with non-overlapping news domains demonstrate FNDCD's effectiveness in improving generalization across domains.
• Score: 10.711567616912312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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