SSN _ IT _ HATE @ LT - EDI -2025: Caste and Migration Hate Speech Detection (2025)

Abstract: This paper proposes a transformer-based methodology for detecting hate speech in Tamil, developed as part of the shared task on Caste and Migration Hate Speech Detection. Leveraging the multilingual BERT (mBERT) model, we fine-tune it to classify Tamil social media content into caste/migration-related hate speech and non hate speech categories. Our approach achieves a macro F1-score of 0.72462 in the development dataset, demonstrating the effectiveness of multilingual pretrained models in low-resource language settings. The code for this work is available on github Hate-Speech Deduction.

## introduction hate speech poses a threat to marginalized communities, especially those affected by caste discrimination and migration. in india, these sensitive issues often fuel online hate, commonly expressed in regional languages like tamil. addressing such content is vital to fostering respectful digital spaces. automated detection of hate speech in tamil presents challenges due to its lowresource nature, complex morphology, frequent code-mixing with english, and informal writing style. existing tools and datasets often prioritize high-resource languages, leaving dravidian languages underrepresented. we propose a multilingual transformer-based system to identify casteand migration-related hate speech in tamil social media. using a curated, annotated dataset, we finetune the bert-base-multilingual-cased model with bert tokenization, cross-entropy loss, and evaluate performance via standard metrics. predictions on unseen test data gauge generalization ability. this study contributes to ethical ai by addressing identity-based harm in underrepresented languages. by applying advanced nlp methods, we aim to promote safer, more inclusive online platforms for vulnerable groups. ## literature survey hate speech detection has emerged as a vital area of natural language processing (nlp), focusing on identifying abusive, derogatory, or inciting content across multiple platforms and languages. early work in this field primarily employed statistical machine learning models, such as support vector machines (svm), naive bayes, and logistic regression, with hand-crafted features like ngrams and tf-idf vectors @xcite . with the advent of deep learning, models such as convolutional neural networks (cnns) and long short-term memory networks (lstms) demonstrated superior performance, especially on noisy and informal texts common in social media @xcite . however, these models often struggled with contextual understanding and multilingual settings. transformer-based models like bert @xcite revolutionized nlp by introducing contextualized embeddings and enabling transfer learning. these models improved the performance of hate speech classification in multiple languages. @xcite for low-resource languages, multilingual variants like mbert and xlm-roberta @xcite are particularly valuable. in the indian context, @xcite studied hate speech in tamil and malayalam, addressing challenges such as code-mixing, orthographic variation, and dialect diversity. @xcite developed transformer-based systems for abusive comment @xcite proposed a deep ensemble framework for detecting hate in multiple dravidian languages. a landmark overview by @xcite detailed the objectives, dataset structure, and methodological landscape of the lt-edi shared task, with specific focus on caste and migration hate speech in tamil. comprehensive surveys, such as @xcite , have discussed key limitations in early hate speech research, advocating for more context-aware models. @xcite emphasize the importance of robust datasets and suggest improvements in annotation quality and domain adaptation. several recent works have emphasized ethical concerns, bias mitigation, and fairness in hate speech classifiers @xcite . (jahan and oussalah, 2023) highlight ethical pitfalls and recommend model transparency and explainability. multimodal approaches @xcite ; @xcite ) combining text with images or audio have shown that non-textual features such as tone, pitch, and visual cues can enhance hate detection. while promising, these approaches are more resource-intensive and less feasible in textonly shared tasks. further, enhancements in semantic understanding like sentiment integration @xcite and contextual embeddings @xcite have contributed to improved classification accuracy. techniques like smote for balancing class distributions @xcite also play a critical role when dealing with imbalanced hate speech datasets. taken together, these contributions form the basis of our methodological decisions for this shared task submission. ## experiment and results all experiments were carried out using pytorch and hugging face transformers on an nvidia tesla v100 gpu, following the fine-tuning phase. ## limitations while our model performs well, it has notable limitations. tamil social media posts often blend languages and include slang or informal expressions, which can confuse the model, especially when hate is subtly or sarcastically conveyed. the dataset used is small and only labels posts as hate or nonhate, overlooking the nuances in harmful expression. since the model relies on mbert, it struggles with cultural context and its predictions can be diffi-cult to interpret. this raises concerns about fairness and bias, particularly if it learns problematic patterns from the training data. social media often mirrors societal biases, which the model may unintentionally reinforce. though we didn't perform an in-depth bias or fairness analysis in this study, exploring variations across caste, gender, or identity groups is a vital direction for future work. a deeper investigation into dataset and model biases could support fairer and more responsible deployment. ## conclusion our work shows that a multilingual model like mbert can be fine-tuned to effectively detect caste and migration-related hate speech in tamil social media posts. even with limited data and the challenges of informal, mixed-language text, the model achieved good performance. this approach highlights the potential of using existing language models to support low-resource languages and address real social issues. we hope our method encourages more research in this area and helps make online spaces safer and more inclusive for everyone.