A Systematic Literature Review on Fake News in the COVID-19 Pandemic: Can AI Propose a Solution?

Abstract

The COVID-19 pandemic has led to an incredible amount of fake news and conspiracy theories around the world. Calls for the integration of COVID-19 and fake news-related research have been advanced in various fields. This paper aims to unpack a structured overview of previous research topics and findings and identify gaps. Our goal in this systematic review is to (a) synthesize the selected earlier studies, (b) offer researchers a structural framework for future COVID-19 and fake news research, and (c) recommend relevant areas for future research. In this study, we focus on eighty conceptual and empirical studies on misinformation of COVID-19-related news on social media. We identify vital publications and methodological and theoretical approaches that exist in the COVID-19 literature. The articles were systematically analyzed, focusing on the research context and time frame, data collection/analysis procedures, and equivalence issues. While COVID-19 research has been advancing significantly over the past couple of months, numerous questions remain unexplained in the domain of the social media landscape. For example, our review suggests that researchers should begin to concentrate on a process framework blending Artificial Intelligence (AI) to curb the fake news problem. This can be achieved in all three phases, e.g., the study of individual decisions and experiences, the experiences of groups and organizations and the interactions between them, and finally, the interactions at the broadest level (micro, meso, and macro stages).