Towards a Safer Conversation Space: Detection of Toxic Content in Social Media (Student Consortium)

With content on social media turning increasingly toxic, it has attracted intensive research in the Natural Language Processing domain to detect aggression, hate, profanity, insult, cyberbullying and other personal attacks. Unlike most of the work in toxic content detection where the nature of toxicity is determined, we treat the detection of toxic content as a binary classification task. Here, we have explored Support Vector Machine, Boosting and deep neural networks for classification. We have trained the model on twitter datasets. With a goal of better predictive performance, our approach uses a majority voting ensemble to aggregate the predictions of individual classifiers.