Automatic Misogyny Detection in Social Media Platforms using Attention-based Bidirectional-LSTM

The important growth of social media and online gaming sites in recent years have increased the challenge of online moderation to keep the internet safe and without toxic content. Today, machine learning techniques play an important role in detecting inappropriate content and help moderate online interaction. Text classification using Natural Language Processing (NLP) methods has been extensively studied using deep learning models and transformers which have shown impressive results. Despite this, specific classification tasks on limited datasets still need to be improved. In this paper, we propose an approach based on an Attention-Based Bidirectional LSTM model and a combination of custom features to enhance automatic misogyny identification (AMI) on social media. We present a multi-lingual study of the phenomena by carrying out different classification experiments. Our study focuses on selecting most important features to improve the model for misogyny detection. The proposed model outperforms many state-of-the-art approaches across multiple datasets.