Comment toxicity detection via a multichannel convolutional bidirectional gated recurrent unit

Abstract

Recently, toxicity identification has become the most serious problem in online communities and social networking sites. Therefore, an automatic toxic identification system needs to be developed for preventing and limiting users from these online environments. In this paper, we present a multichannel convolutional bidirectional gated recurrent unit (MCBiGRU) for detecting toxic comments in a multilabel environment. The proposed model generates word vectors using pre-trained word embeddings. Moreover, this hybrid model extracts local features with many filters and different kernel sizes to model input words with long term dependency. We then integrate multiple channels with a fully connected layer, normalization layer, and an output layer with a sigmoid activation function for predicting multilabel categories. The experimental results indicate that the proposed MCBiGRU model outperforms in terms of multilabel metrics.