Looking Behind the Mask

Abstract

With the facilities of social media platforms like Facebook, Twitter, Google+, YouTube etc. people are capable of expressing their views & news, sharing moments via photos, liking, commenting and sharing others posts. The online social networks (OSNs) are not only giving positive supports to its users, but also creating opportunities to assassin personals by the trolls. Trolls are usually the OSN users who try to hide themselves while doing bad comments, false accusations, starting controversies, spreading fake news or rumors which could be considered as character assassination of individuals. The online behavior of an OSN user could be tracked via his/her digital footprints. Though tracking huge number of users who are generating billions of textual and image data every day, could be considered as a challenging task. In this paper, we have proposed a novel detection system for identifying character assassination from social media platforms. The proposed method first predicts the personality traits using users' textual data. Therefore, LIWC, SlangNet, SentiWordNet, SentiStrength, Colloquial WordNet has been utilized as a psycholinguistic tool. LIWC-based feature engineering has been performed on the comments of the trolls as well as the victim user. SlangNet and Colloquial WordNet are used for detecting English slang words in the comments as it is evident that slangs are the basic communicative way to defame someone.