Multi-contextual learning in disinformation research: A review of challenges, approaches, and opportunities

Though a fair amount of research is being done to address disinformation in online social media, it has so far managed to stay ahead of the researchers’ learning curves forcing the publishers to rely on manual effort to a large extent. The root cause lies in the complex multi-contextual nature of the problem. The way a disinformation propagates on the social graph depends on multiple contexts i.e., content of the original news, credibility of the news source, poster of the message referring the news, message content, recipients of message with their social as well as psychological backgrounds, the role played by the available knowledge, and the temporal as well as the propagation pattern while the message becomes viral on the social graph. This article reviews each of these contexts to define the multi-contextual learning problem and summarizes the work done using each of them. Multi-contextual learning gets exacerbated by few other challenges. This article also reviews the approaches adopted so far to tackle each of these challenges along with an exhaustive review of the multi-contextual learning strategies adopted so far. The multi-contextuality aspect as well as the related challenges are horizontal in nature across the three primary verticals of disinformation i.e., fake news, rumor, and propaganda. Existing review articles primarily tackle one of these verticals in isolation with one or few of the above mentioned contexts. Also the related challenges have not seen any focused review so far. This article seeks to address these gaps by offering a comprehensive systemic view across this domain and concludes with a list of future research directions.