A systematic mapping on automatic classification of fake news in social media

Social media has become the primary source for rumor spreading, and information quality is an increasingly important issue in this context. In the last years, many researchers have been working on methods to improve the rumor classification, especially on the identification of fake news in social media, with good results. However, due to the complexity of natural language, this task presents difficult challenges, and many research opportunities. This survey analyzes 87 distinct publications, which were systematically selected out of 1333 candidates. This work covers eight years of research on fake news applied in social media and presents the main methods, text and user features, and datasets used in literature.