As cyberbullying becomes more and more frequent in social networks, automatically detecting it and pro-actively acting upon it becomes of the utmost importance. In this work, we study how a recent technique with proven success in similar tasks, Fuzzy Fingerprints, performs when detecting textual cyberbullying in social networks. Despite being commonly treated as binary classification task, we argue that this is in fact a retrieval problem where the only relevant performance is that of retrieving cyberbullying interactions. Experiments show that the Fuzzy Fingerprints slightly outperforms baseline classifiers when tested in a close to real life scenario, where cyberbullying instances are rarer than those without cyberbullying.
Hugo Rosa, is a PhD student in Computer Science from Instituto Superior Técnico (IST). His current field of study is automatic textual cyberbullying detection in social networks, which constitutes a natural continuation to his previous research and Master Thesis (also from IST in 2014), topic detection in social networks. His research topics of interest include Machine Learning, Natural Language Processing, Social Networks Data Mining, Fuzzy Systems and Deep Learning
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