  Combating fake news needs a variety of defense methods. Although rumor
detection and various linguistic analysis techniques are common methods to
detect false content in social media, there are other feasible mitigation
approaches that could be explored in the machine learning community. In this
paper, we present open issues and opportunities in fake news research that need
further attention. We first review different stages of the news life cycle in
social media and discuss core vulnerability issues for news feed algorithms in
propagating fake news content with three examples. We then discuss how
complexity and unclarity of the fake news problem limit the advancements in
this field. Lastly, we present research opportunities from interpretable
machine learning to mitigate fake news problems with 1) interpretable fake news
detection and 2) transparent news feed algorithms. We propose three dimensions
of interpretability consisting of algorithmic interpretability, human
interpretability, and the inclusion of supporting evidence that can benefit
fake news mitigation methods in different ways.
