  Combating fake news and misinformation propagation is a challenging task in
the post-truth era. News feed and search algorithms could potentially lead to
unintentional large-scale propagation of false and fabricated information with
users being exposed to algorithmically selected false content. Our research
investigates the effects of an Explainable AI assistant embedded in news review
platforms for combating the propagation of fake news. We design a news
reviewing and sharing interface, create a dataset of news stories, and train
four interpretable fake news detection algorithms to study the effects of
algorithmic transparency on end-users. We present evaluation results and
analysis from multiple controlled crowdsourced studies. For a deeper
understanding of Explainable AI systems, we discuss interactions between user
engagement, mental model, trust, and performance measures in the process of
explaining. The study results indicate that explanations helped participants to
build appropriate mental models of the intelligent assistants in different
conditions and adjust their trust accordingly for model limitations.
