Towards a Personalized Online Fake News Taxonomy

Fake news has become a serious and destabilizing problem in our increasingly polarized society. The core tasks of detecting and characterizing untrue or misleading online content are quite challenging. News consumers intervention has been identified as a crucial addition to Fake News Detection Systems (FNDS). However, even though detection explanation is starting to gain research momentum, not as much attention has been given to personalization of explanations. As humans are the obvious targets of fake news, explanations that can evoke emotional responses and/or are aligned with individual personality traits and cognitive styles can be leveraged to nudging of the news consumer into a reflective state about the subject, which has been shown to be more effective than the crude presentation of facts in changing pre-conceived beliefs. This paper adds to the main goal of misinformation detection systems, aiming to expand them onto personalized FNDS. It proposes a metric to be used in their evaluation. It offers a definition to help research on the implementation of personalized fake news explanations. And finally, it proposes a personalized fake news taxonomy, discussing its components centered around emotion-based and personality-based explanations. This taxonomy highlights several opportunities for those researching in the area of personalized fake news explanation systems.