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, a detailed look at the current state-of-the-art in cyberbullying detection reveals that deep learning techniques have seldom been used to tackle this problem, despite growing reputation in other text-based classification tasks. Motivated by neural networks' documented success, three architectures are implemented from similar works: a simple CNN, a hybrid CNN-LSTM and a mixed CNN-LSTM-DNN. In addition, three text representations are trained from three different sources, via the word2vec model: Google-News, Twitter and Formspring. The experiment shows that these models with one of the above embeddings beat other benchmark classifiers (Support Vector Machines and Logistic Regression) both in an unbalanced and balanced version of the same dataset.

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.