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The wide dissemination of fake news is increasingly threatening bothindividuals and society. Fake news detection aims to train a model on the pastnews and detect fake news of the future. Though great efforts have been made,existing fake news detection methods overlooked the unintended entity bias inthe real-world data, which seriously influences models' generalization abilityto future data. For example, 97\% of news pieces in 2010-2017 containing theentity `Donald Trump' are real in our data, but the percentage falls down tomerely 33\% in 2018. This would lead the model trained on the former set tohardly generalize to the latter, as it tends to predict news pieces about`Donald Trump' as real for lower training loss. In this paper, we propose anentity debiasing framework (\textbf{ENDEF}) which generalizes fake newsdetection models to the future data by mitigating entity bias from acause-effect perspective. Based on the causal graph among entities, newscontents, and news veracity, we separately model the contribution of each cause(entities and contents) during training. In the inference stage, we remove thedirect effect of the entities to mitigate entity bias. Extensive offlineexperiments on the English and Chinese datasets demonstrate that the proposedframework can largely improve the performance of base fake news detectors, andonline tests verify its superiority in practice. To the best of our knowledge,this is the first work to explicitly improve the generalization ability of fakenews detection models to the future data. The code has been released athttps://github.com/ICTMCG/ENDEF-SIGIR2022.

Generalizing to the Future: Mitigating Entity Bias in Fake News Detection