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In the Social Web scenario, large amounts of User-Generated Content (UGC) arediffused through social media often without almost any form of traditionaltrusted intermediaries. Therefore, the risk of running into misinformation isnot negligible. For this reason, assessing and mining the credibility of onlineinformation constitutes nowadays a fundamental research issue. Credibility,also referred as believability, is a quality perceived by individuals, who arenot always able to discern, with their own cognitive capacities, genuineinformation from fake one. Hence, in the last years, several approaches havebeen proposed to automatically assess credibility in social media. Many of themare based on data-driven models, i.e., they employ machine learning techniquesto identify misinformation, but recently also model-driven approaches areemerging, as well as graph-based approaches focusing on credibilitypropagation, and knowledge-based ones exploiting Semantic Web technologies.Three of the main contexts in which the assessment of information credibilityhas been investigated concern: (i) the detection of opinion spam in reviewsites, (ii) the detection of fake news in microblogging, and (iii) thecredibility assessment of online health-related information. In this article,the main issues connected to the evaluation of information credibility in theSocial Web, which are shared by the above-mentioned contexts, are discussed. Aconcise survey of the approaches and methodologies that have been proposed inrecent years to address these issues is also presented.

Information Credibility in the Social Web: Contexts, Approaches, and Open Issues