In the digital age, social media platforms like Twitter serve as an extensive repository of public discourse, including instances of sexism. It is important to identify such behavior since radicalized ideologies can lead to real-world violent acts. This project aims to develop a deep learning-based tool that leverages a combination of BERT (both English and multilingual versions) and GraphSAGE, a Graph Neural Network (GNN) model, alongside sentiment analysis and natural language processing (NLP) techniques. The tool is designed to analyze tweets for sexism detection and classify them into five categories.

With the rise of Generative AI (GenAI) models in recent years, it is necessary to understand how they performed compared with other Deep Learning techniques, across tasks and across different languages. In this study, we benchmark ChatGPT-4 and XML-RoBERTa, a multilingual transformer-based model, as part of the Multilingual Binary and Multiclass Hope Speech Detection within the PolyHope-M 2025 competition. Furthermore, we explored prompting techniques and data augmentation to determine which approach yields the best performance. In our experiments, XML-RoBERTa frequently outperformed ChatGPT-4. It also attained F1 scores of 0.86 for English, 0.83 for Spanish, 0.86 for German, and 0.94 for Urdu in Task 1, while achieving 0.73 for English, 0.70 for Spanish, 0.69 for German, and 0.60 for Urdu in Task 2.