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Combating fake news needs a variety of defense methods. Although rumordetection and various linguistic analysis techniques are common methods todetect false content in social media, there are other feasible mitigationapproaches that could be explored in the machine learning community. In thispaper, we present open issues and opportunities in fake news research that needfurther attention. We first review different stages of the news life cycle insocial media and discuss core vulnerability issues for news feed algorithms inpropagating fake news content with three examples. We then discuss howcomplexity and unclarity of the fake news problem limit the advancements inthis field. Lastly, we present research opportunities from interpretablemachine learning to mitigate fake news problems with 1) interpretable fake newsdetection and 2) transparent news feed algorithms. We propose three dimensionsof interpretability consisting of algorithmic interpretability, humaninterpretability, and the inclusion of supporting evidence that can benefitfake news mitigation methods in different ways.

Open Issues in Combating Fake News: Interpretability as an Opportunity