  Recent years have witnessed remarkable progress towards computational fake
news detection. To mitigate its negative impact, we argue that it is critical
to understand what user attributes potentially cause users to share fake news.
The key to this causal-inference problem is to identify confounders --
variables that cause spurious associations between treatments (e.g., user
attributes) and outcome (e.g., user susceptibility). In fake news
dissemination, confounders can be characterized by fake news sharing behavior
that inherently relates to user attributes and online activities. Learning such
user behavior is typically subject to selection bias in users who are
susceptible to share news on social media. Drawing on causal inference
theories, we first propose a principled approach to alleviating selection bias
in fake news dissemination. We then consider the learned unbiased fake news
sharing behavior as the surrogate confounder that can fully capture the causal
links between user attributes and user susceptibility. We theoretically and
empirically characterize the effectiveness of the proposed approach and find
that it could be useful in protecting society from the perils of fake news.
