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    Classifying cyber aggression in social media posts

    Fortunatus, Meisy
    Abstract
    Cyber aggression is one of the most prevalent issues stemmed from the growing number of internet users. Given the numerous amount of online posts every day, it is not feasible to detect textual cyber aggression manually. This research focuses on analysing social media posts to find elements of cyber aggression and then build an algorithm that uses these elements in a set of rules to detect cyberbullying effectively. Lexicon enhanced rule-based method is used to detect cyber aggression on three different types of social media textual communication: single post from Facebook, question and answer pair from Formspring.me, and thread style from MySpace.com. The algorithm is evaluated using a combination of accuracy, precision, recall, and F1 measure. It was found that the algorithm performed best for single style data and the least for thread style data.... [Show full abstract]
    Keywords
    cyber aggression; textual aggression; harrassment; emoticon sentiment; lexicon; social media; aggression detection; cyber; bullying; cyber aggression; sentiment analysis
    Fields of Research
    080107 Natural Language Processing; 080709 Social and Community Informatics; 080309 Software Engineering
    Date
    2019
    Type
    Thesis
    Collections
    • Masters Theses [849]
    • Department of Informatics and Enabling Technologies [119]
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