Open Issues in Combating Fake News: Interpretability as an Opportunity
It addresses the problem of fake news propagation for social media users and researchers, but is incremental as it reviews existing issues and suggests opportunities without presenting new results.
The paper identifies open issues in combating fake news, focusing on vulnerabilities in news feed algorithms and proposing interpretable machine learning as a solution, including interpretable detection and transparent algorithms.
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.