Automatic detection of hate speech in code-mixed Indian languages in twitter social media interaction using DConvBLSTM-MuRIL ensemble method

Social media platforms have gained immense popularity in recent years and are used for various activities such as marketing, news-sharing, and celebrating achievements. However, they are also notorious for spreading hateful and discriminatory content, which can cause harm to individuals and communities. Therefore, it is crucial to detect and remove such content from social media platforms as soon as possible. Although research related to the detection of hate speech and inflammatory content is increasing, studies focused on code-mixed Indian languages are limited. Hence, in this work, we have conducted a comprehensive study, where we have compared the effectiveness of various neural networks, and transformer-based techniques for the detection of hate and objectionable language in social media tweets in Hinglish, Tamil written in English, and Malayalam written in English, to propose the best-performing ensemble model, named as DConvBLSTM-MuRIL. To carry out our experiments, we have created our datasets for the three languages under study and compared the results with already existing datasets. Our proposed weighted ensemble framework outperformed the existing models, achieving better-weighted F1-scores and better accuracy for all the three languages under consideration.