Abstract
One of the major problems, apparent in online social media, is the toxic online content. This has continued unabated, as people from diverse cultural backgrounds access the Internet, concealing their identity under the cloud of anonymity. Deep neural networks have been employed to detect hate speech from online content. This paper describes three different Deep Neural Network (DNN) Architectures for detection of hate words in Twitter - Gated Recurrent Unit (GRU), useful in capturing sequence orders, Convolution Neural Network (CNN), good for feature extraction, and Universal Language Model Fine-tuning (ULMFiT) model, which is based on transfer learning technique. ULMFiT model uses the DNN Architecture called Average-SGD Weight-Dropped Long Short Term Memory (AWD-LSTM). AWD –LSTM model was pre-trained using WikiText103 dataset. This method significantly outperformed the other Architectures.
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