CUET _ N 317@ LT - EDI 2025: Detecting Hate Speech Related to Caste and Migration with Transformer Models (2025)

Abstract: Language that criticizes threatens, or discriminates against people or groups because of their caste, social rank, or status is known as caste and migration hate speech and it has grown in credibly common on social media. Such speech not only contributes to social disruption and in equity, but it also puts at risk the safety and mental health of the targeted groups. Due to the absence of labeled data, the subtlety of culturally unique insults, and the lack of strong linguistic resources for deep text recognition, it is especially difficult to detect caste and migration hate speech in low-resource Dravidian languages like Tamil. In this work, we address the Caste and Migration Hate Speech Detection task, aiming to automatically classify user-generated content as either hateful or non-hateful. We evaluate a range of approaches, including a traditional TF-IDF-based machine learning pipeline using SVM and Logistic Regression, alongside five transformer-based models: mBERT, XLM-R, MuRIL, Tamil BERT, and Tamilhate-BERT.Among these, the domain-adapted Tamilhate BERT achieved the highest macro-F1 score of 0.88 on the test data, securing 1st place in the Shared Task on Caste and Migration Hate Speech Detection at DravidianLangTech@LT-EDI 2025. Our findings highlight the strong performance of transformer models, particularly those fine-tuned on domain-specific data, in detecting nuanced hate speech in low-resource, code-mixed languages like Tamil.

## introduction caste and migration related hate speech is defined as language that insults, threatens, or discriminates against individuals or groups based on their caste, social status, or immigration background, has be-come increasingly prevalent on social media @xcite . this type of speech affects mental health in a very bad way. so, detection of caste-and migration-related hate speech is very crucial. the automatic hate speech tool may be useful to prevent such activities. detecting hate speech in a low-resource lan-guage like tamil is very challenging due to lim-ited resources. in addition, our task was to identify only caste-and migration-related hate speech. to find out whether a sentence expresses hate or not it is crucial to understand the intent of the language @xcite . in tamil, it is quite difficult to identify compared to high-resource language like english. in this study, we have fine-tuned several models to facilitate automatic hate speech detection in tamil. ## related work there have several research recently in identifying hate speech related to caste and migration in tamil. transformer models @xcite have become foundational, with several recent studies exploring their efficacy in this specific domain. for the lt-edi 2024 shared task on caste and migration hate speech detection in tamil (rajiakodi et al.), alam et al. (2024) investigated various models, finding m-bert to achieve a macro f1score of 0.80. in the same shared task, singhal and bedi (2024) demonstrated the power of ensembling transformer-based models (xlm-r, mbert, muril) through majority voting, securing the 1st rank with a macro f1-score of 0.82. beyond monolingual text, hossain et al. (2025a) explored multimodal fusion for telugu hate speech, also reporting a strong unimodal text baseline with mbert (f1-score 0.4968). the challenge of codemixed dravidian languages was addressed by sreelakshmi et al. (2024), who found a combination of muril embeddings and an svm classifier to be highly effective, achieving accuracies up to 96%. for indonesian, another context outside of highresource languages, hakim et al. (2024) combined indobertweet with bilstm and cnn, yielding an f1-score of 85.06%. ensemble strategies remain popular; roy et al. (2022) proposed a weighted ensemble of bert models and a deep neural network for offensive and hate speech in tamil and malayalam code-mixed data, achieving high f1-scores. highlighting the challenges in low-resource settings, reddy et al. (2024) investigated data augmentation and noted the ineffectiveness of pos tagging for dravidian languages. multimodal approaches have also been investigated by hossain et al. (2025b), who propose a transformer-based multimodal fusion model with cross-modal attention for hate speech detection. ## dataset and task description the dataset @xcite for the caste and migration hate speech detection task consists of mixed tamil-english social media comments that have been annotated to indicate whether hate speech related to caste or migration is present (1) or not (0). three separate sets of data are offered: training, development, and test. the class-wise distribution of the dataset is summed up in ## system overview text classification tasks are currently very challenging for low-resource languages in social media like tamil. therefore, to detect tamil hate speech related to caste and migration, we followed two distinct paths: a traditional machine learning approach as a baseline and advanced transformer-based approaches to handle the complexities of the text corpus. the figure depicts the overall process flow that we applied to do the task. ## result and analysis ## error analysis we conducted a detailed error analysis to better understand the strengths and limitations of our bestperforming model. ## conclusion in this study, we analyzed three different methodologies for detecting caste and migration related hate speech in tamil. among these approaches, our second method tamilhate-bert, emerged as the top performer with a macro f1 score of 0.88, outperforming both the ml baseline and the transformer of ensemble. these findings highlight the power of transformers, especially when they are adapted to the specific linguistic and cultural characteristics of the task. for future work, we recommend enlarging the dataset with more varied examples, exploring multi-modal inputs to capture richer context, and devising strategies to further mitigate model bias.