  The wide dissemination of fake news is increasingly threatening both
individuals and society. Fake news detection aims to train a model on the past
news and detect fake news of the future. Though great efforts have been made,
existing fake news detection methods overlooked the unintended entity bias in
the real-world data, which seriously influences models' generalization ability
to future data. For example, 97\% of news pieces in 2010-2017 containing the
entity `Donald Trump' are real in our data, but the percentage falls down to
merely 33\% in 2018. This would lead the model trained on the former set to
hardly 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 an
entity debiasing framework (\textbf{ENDEF}) which generalizes fake news
detection models to the future data by mitigating entity bias from a
cause-effect perspective. Based on the causal graph among entities, news
contents, and news veracity, we separately model the contribution of each cause
(entities and contents) during training. In the inference stage, we remove the
direct effect of the entities to mitigate entity bias. Extensive offline
experiments on the English and Chinese datasets demonstrate that the proposed
framework can largely improve the performance of base fake news detectors, and
online tests verify its superiority in practice. To the best of our knowledge,
this is the first work to explicitly improve the generalization ability of fake
news detection models to the future data. The code has been released at
https://github.com/ICTMCG/ENDEF-SIGIR2022.
