Curated Hinglish Dataset for Deep Learning-Based Misogyny Detection - Negi, Deepti; Maheshwari, Himani; Arya, Chandrakala; Chandra, Umesh; Shukla, Gaurav (Journal of Reliability and Statistical Studies, 2026)
Social networking sites serves as influential medium for sharing information and communication; however, their mostly unregulated and open frameworks have also turned them into fertile ground for the dissemination of offensive content. The simplicity of sharing content, coupled with user anonymity and vast reach, facilitates the swift circulation of offensive, abusive, and discriminatory remarks. Engagement-driven algorithms may unintentionally promote such harmful content, increasing its visibility and impact. Consequently, offensive content on platforms like Twitter, YouTube, Facebook, and Reddit frequently gains traction, fuelling online hostility, social division, and tangible real-world effects. Offensive content about women is a prevailing subject on social media platforms. Instances of misogyny are disproportionately represented on social media platforms and misogyny is a substantial societal concern which needs to be addressed. While exhaustive research work has been done for offensive language detection in monolingual settings, the domain of misogyny detection in code-mixed texts is relatively underexplored and there is lack of studies that tackle misogyny detection in under-resourced languages. One of the major causes is unavailability of appropriate Hindi-English mixed-coded language dataset. Therefore, in attempt to bridge this research gap our study focuses on developing a dataset and leveraging deep learning techniques on this high-quality curated dataset containing Hindi-English code-mixed comments from multiple social media platforms. This dataset contains 17,234 comments from different social media platforms, annotated manually into misogynistic and non-misogynistic based on the content. Our study also demonstrates a detailed comparison between baseline machine learning, deep learning, and transformer-based approaches utilising our own curated Hinglish dataset. The results indicated that fine-tuned BERT outperformed the deep learning algorithms with highest 0.92 accuracy.