Solvers@ LT - EDI -2025: Caste and Migration Hate Speech Detection in T amil- E nglish Code-Mixed Text (2025)

Abstract: Hate speech detection in low-resource languages such as Tamil presents significant challenges due to linguistic complexity, limited annotated data, and the sociocultural sensitivity of the subject matter. This study focuses on identifying caste- and migration-related hate speech in Tamil social media texts, as part of the LT-EDI@LDK 2025 Shared Task. The dataset used consists of 5,512 training instances and 787 development instances, annotated for binary classification into caste/migration-related and non-caste/migration-related hate speech. We employ a range of models, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and transformer-based architectures such as BERT and multilingual BERT (mBERT). A central focus of this work is evaluating model performance using macro F1-score, which provides a balanced assessment across this imbalanced dataset. Experimental results demonstrate that transformer-based models, particularly mBERT, significantly outperform traditional approaches by effectively capturing the contextual and implicit nature of hate speech. This research underscores the importance of culturally informed NLP solutions for fostering safer online environments in underrepresented linguistic communities such as Tamil.

## introduction detecting hate speech in low-resource languages such as tamil is a complex and crucial task, especially in the context of caste-and migration-related discrimination. tamil, predominantly spoken in tamil nadu (india), sri lanka, and among diasporic communities, is rich in cultural and linguistic diversity, which poses unique challenges to natural language processing (nlp). in recent years, the spread of hate speech targeting caste and migrant communities has escalated on social media platforms, demanding robust automatic detection systems that are socially aware and ethically grounded. the identification of such harmful content is complicated by the nuanced ways in which caste and migration are discussed, often involving implicit language, sarcasm, and regional idioms. additionally, tamil's morphological richness, the scarcity of annotated corpora, and the limited availability of linguistic tools make it difficult to develop high-performance hate speech classifiers. to address these issues, the lt-edi@ldk 2025 shared task released a manually annotated dataset in tamil, categorizing instances as either caste/migration-related hate speech or noncaste/migration-related hate speech @xcite . in this study, we evaluate the performance of several machine learning and deep learning models-specifically, support vector machines (svm), convolutional neural networks (cnn), and transformer-based architectures such as bert and multilingual bert (mbert)-for the task of hate speech classification @xcite . given the class imbalance and the sensitive nature of the content, we use macro f1-score as the primary evaluation metric. our findings show that transformer-based models, particularly mbert, perform significantly better at capturing contextual cues and implicit hate. this work contributes to the growing body of research aimed at improving online safety in underrepresented linguistic communities and emphasizes the importance of culturally and ethically grounded nlp approaches to hate speech detection. ## literature survey hate speech detection is an increasingly important task in natural language processing (nlp), particularly with the rise of social media platforms where offensive and abusive content is frequently encountered. while substantial progress has been made in hate speech detection for high-resource languages like english, the task remains underdeveloped for low-resource languages such as tamil. early shared tasks like hasoc and offenseval laid foundational work in this domain @xcite . tamil poses unique challenges due to its rich morphology, codemixed usage with english, and the cultural sensitivity of topics like caste and migration. the scarcity of large, annotated datasets in tamil further complicates model development for hate speech classification. recent shared tasks such as the lt-edi (language technology for equality, diversity, and inclusion) series have helped bring attention to the issue, offering benchmark datasets and encouraging the development of hate speech detection systems specifically for tamil and other dravidian languages @xcite . these initiatives have laid the groundwork for evaluating traditional machine learning, deep learning, and transformerbased approaches on nuanced categories such as caste and migration hate speech. ## results and discussion the hate speech detection experiments using tamil-english code-mixed data demonstrate that transformer-based models, particularly multilingual bert (mbert), achieve the highest performance compared to traditional and shallow learning models. mbert excels in capturing complex, context-rich semantic structures within codeswitched and informal text. deep learning models such as cnn and bilstm performed moderately well, capturing local and sequential patterns, respectively. traditional models such as svm, logistic regression, and knn were able to handle basic binary discrimination but struggled with finegrained category separation. ## conclusion in this study, we addressed the challenge of hate speech detection in tamil-english code-mixed social media text, a complex task due to linguistic diversity, informal language, and limited annotated resources. we implemented and evaluated a classification pipeline using a combination of traditional and modern techniques, including support vector machines (svm), convolutional neural networks (cnn), and transformer-based models such as bert and multilingual bert (mbert). our experiments demonstrated that transformerbased models, particularly mbert, achieved the best overall performance, effectively capturing contextual and semantic nuances in code-mixed text. cnns provided competitive results by modeling local syntactic patterns, while svm served as a strong baseline for traditional feature-based learning in low-resource conditions. despite the encouraging results, several challenges remain unresolved, including class imbalance, informal code-switching, and the limited size of annotated datasets. future work may focus on incorporating domain-adaptive pretraining, leveraging data augmentation strategies, and employing multilingual knowledge integration to further improve model robustness. this research contributes to the development of effective nlp solutions for underrepresented south asian languages and supports broader efforts to ensure safer, more inclusive online spaces.