A Context-Aware and Target-Adaptive Multilingual Framework for Hate Speech Detection in Code-Switched Social Media Text

The rapid expansion of social media has accelerated the spread of hate speech, particularly within multilingual and code-switched environments where users frequently alternate languages within a single conversation. Detecting this kind of content is still challenging because of the use of multiple languages, the lack of clear context, and the difficulty of identifying the intended target of the hate speech. Current methods for detecting hate speech, such as traditional machine learning models and transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Cross-lingual Language Model-RoBERTa (XLM-R), have improved contextual understanding. However, they still struggle to accurately identify the intended target of hate speech and lack fine-grained context awareness. This limitation reduces the effectiveness of implicit hate speech detection and results in more false positives, especially in multilingual and low-resource settings. To tackle these issues, this research introduces a Context-Aware and Target-Adaptive Multilingual Hate Speech Detection (CTM-HSD) model. The proposed method combines multilingual transformer-based embeddings with a context-aware attention mechanism to capture semantic dependencies in text. It also includes a separate target identification module to identify the person or group being targeted. In addition, adaptive learning techniques, such as transfer learning and data augmentation, are used to improve performance in low-resource and code-switched scenarios. Evaluation on multilingual and code-switched datasets shows that the proposed model outperforms baseline models such as BERT and XLM-R, with an accuracy of 92.4% and an F1-score of 91.2%. The findings demonstrate that the integration of contextual awareness and target adaptability markedly enhances the identification of implicit hate speech and reduces false positives. The proposed framework offers a robust and flexible solution for real-world multilingual content moderation systems.