Misinformation Detection in Social Networks: A Systematic Literature Review

Abstract

Information quality is becoming an increasingly important issue as social networks have become the primary source for misinformation dissemination. Because of their ease of use, spreading behavior, and low cost, social network platforms are leveraging news consumption. Because of its negative impact on society, this term became more popular and dangerous following the 2016 U.S. presidential election. Many studies have been developed on methods to improve rumor classification, particularly on misinformation detection on social media, with promising results in recent years. Despite the growth of this type of research, it is difficult for a researcher to identify the most up-to-date literature on misinformation detection. To address this challenge, this paper presents a systematic review of the literature that provides an overview of this research area and analyzes high-quality research papers on fake news detection. According to our search protocol, more than 670 articles were discovered during this systematic literature review. Then, we put these studies through a series of scanning stages to ensure that they were of high quality. We chose 76 high-quality studies based on our selection process flow diagram, which is described in this paper. This review describes 10 years of research on social media misinformation and presents the main methods and data sets used in the literature.