Abusive Language Detection and Characterization of Twitter Behavior
This addresses the problem of identifying abusive speech in social media for content moderation, but it is incremental as it applies an existing method to a specific domain.
The paper tackled abusive language detection on Twitter using a Bidirectional Recurrent Neural Network (BiRNN), showing it outperformed CNN and RNN methods for this task.
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.