A novel hybrid BGRU-CNN approach for multilabel toxicity detection in online environments

This study aims to develop an automatic system for detecting toxic comments in online environments, particularly on social networking platforms. The focus is on efficiently identifying and categorizing toxicity in user-generated comments to address the growing issue of harmful online content. A novel hybrid model, Text-BGRU-CNN, combining Bidirectional Gated Recurrent Unit (BGRU) and Text Convolutional Neural Network (Text-CNN), is introduced for multilabel toxicity detection. This model uses pre-trained word embeddings for word vector generation and a range of filters to extract local features and long-term dependencies in text. It incorporates a fully connected layer, a normalization layer, and an output layer for multilabel category prediction. The proposed hybrid model demonstrates superior classification accuracy in experimental trials. It was tested on a dataset divided into training and testing sets, enhanced by significant pre-processing. Structural modifications, including increasing dense units and filters, were evaluated. The final model, combining GRUs with a single CNN layer, achieved an accuracy of 0.9944 in classifying toxic comments. The study evidences the efficacy of a hybrid GRU and single-layer CNN model in online toxic comment classification. Results suggest that simpler model architectures, supplemented by extensive pre-processing, yield high accuracy and efficient training. The findings underscore the importance of further research to understand biases in trained classifiers and suggest exploring alternative methods for representing sequences and independent training of sparsely labeled classes in future work.