Abusive language refers to an insult or vulgarity which harass or deceive the target. Social media is a famous platform for the people to express their opinions publicly and to interact with other people in the world. Some of them may misuse their freedom of speech to bully others through abusive language. This will leads to the need for detecting abusive speech. Otherwise, it may severely impact the user?s online experience. It may be a time-consuming task if the detection and removal of such offensive material are done manually. Also, human supervision is unable to deal with large quantities of data. Therefore automatic abusive speech detection has become essential to be addressed effectively. For detecting abusive speech, context accompanying abusive speech is very useful. In this work, abusive language detection in online content is performed using Bidirectional Recurrent Neural Network (BiRNN) method. Here the main objective is to focus on various forms of abusive behaviors on Twitter and to detect whether a speech is abusive or not. The results are compared for various abusive behaviors in social media, with Convolutional Neural Netwrok (CNN) and Recurrent Neural Network (RNN) methods and proved that the proposed BiRNN is a better deep learning model for automatic abusive speech detection

text classification, abusive language, BiRNN, deep learning, natural language processing

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