EM -26@ LT - EDI 2025: Caste and Migration Hate Speech Detection in T amil- E nglish Code-Mixed Social Media Texts (2025)

Abstract: In this paper, we describe the system developed by Team EM-26 for the Shared Task on Caste and Migration Hate Speech Detection at LTEDI@LDK 2025. The task addresses the challenge of recognizing caste-based and migration related hate speech in Tamil social media text, a language that is both nuanced and under resourced for machine learning. To tackle this, we fine-tuned the multilingual transformer XLM-RoBERTa-Large on the provided training data, leveraging its cross-lingual strengths to detect both explicit and implicit hate speech. To improve performance, we applied social media focused preprocessing techniques, including Tamil text normalization and noise removal. Our model achieved a macro F1-score of 0.6567 on the test set, highlighting the effectiveness of multilingual transformers for low resource hate speech detection. Additionally, we discuss key challenges and errors in Tamil hate speech classification, which may guide future work toward building more ethical and inclusive AI systems. The source code is available on GitHub.1

## introduction increased hate speech on digital platforms constitutes a significant threat to the security of marginalised people. harassment speeches are often targeted against people who associate themselves with some sensitive socio-cultural categories, like caste or migration status, in such highly culturally enriched states of india. the tamil language, in its classical roots and its far-reaching use in south india and around the world, is widely used in social media, but is notably missing in many nlp datasets @xcite . the urgent need to identify caste-based and migration-related hate speech in tamil requires the development of culturally sensitive and technologically accurate automated tools @xcite . to address this concern, lt-edi@ldk 2025 launched the shared task on caste and migration hate speech detection as its continuing effort to promote equality, diversity, and inclusion via language technologies @xcite . this work builds on past works on tamil nlp, including shared tasks that have focused on speech recognition and accessibility for vulnerable people in @xcite @xcite . through this line of research, it becomes evident how necessary it is to develop language-specific approaches to ethical and inclusive ai @xcite . therefore, em-26 participated in creating a strong classification model that would be used to identify caste and migration-related hate speech in social media posts in the tamil language. we chose the xlm-roberta-large multilingual transformer model because it promised to work on languages with little resources and morphology @xcite . our strategy consisted of fine-tuning the xlm-roberta-large model with the organisers' labelled data to understand contextual subtleties in tamil and hence identify the complex levels of hate speech existing. our system illustrates the practical use of multilingual pre-trained models for socially essential tasks, even with limited resources, since it achieved a macro f1 score of 0.6567 on the official test set. this paper details our approach, preprocessing steps, error analysis, and reflections on future improvements for caste and migration hate speech detection. ## related work the task of hate speech detection has picked up the pace in low-resource and culturally sensitive languages such as tamil in recent years. as online hate attacks against caste and migration communities grow, making strong classification systems is both a technical and ethical necessity. first, rulebased approaches were not context-aware. changing the workforce and long-term training are becoming major pains. this is what makes a shift to transformer-based models (e.g., xlm-roberta @xcite , mbert @xcite ) so important. @xcite showed that even for the case of zero-shot and few-shot scenarios, multilingual transfer learning was practical. in the indian setting, @xcite presented dravidiancodemix for offensive and sentiment classification in tamil-english and malayalam-english. subsequently, the hope speech detection task @xcite focused on socially inclusive content in dravidian languages. these tasks demonstrated how performance is enhanced through fine-tuned transformers in the code-switched environment. the lt-edi workshop series has been essential in @xcite and @xcite works concerned inclusive technologies for vulnerable communities and hasoc sharing tasks @xcite , they provided multilingual datasets for hate speech in the indo-european and dravidian languages. specialist research has been conducted recently regarding hate speech among tamils: @xcite pointed out the esoteric nature of casteist language, and @xcite deep-published a fine-grained multilingual benchmark. prompt-based learning @xcite and contrastive learning @xcite and @xcite were promising. @xcite created a hate speech system for telugu-english codemixed text, extending the evidence for the effectiveness of transformer-based models in low-resource settings. the lt-edi@ldk 2025 task extends the scope of this field by explicitly targeting caste and migration hate speech. our method with xlm-roberta-large helps toward fairer and cultureattuned nlp solutions. related efforts by the cic-nlp team have demonstrated the effectiveness of multilingual transformer models in detecting ai-generated and deceptive content across diverse languages, including english and dravidian codemixed text @xcite . these studies further support the applicability of transformer-based architectures such as xlm-roberta in addressing socially sensitive and linguistically complex tasks like caste and migration hate speech detection. ## dataset analysis the dataset for our system includes annotated tamil-english code-switched social media posts for caste and migration-related hate speech. the training set contains 5,512 samples, of which 3,415 are labeled non-hate speech (label 0) and 2,097 are labeled caste/migration-related hate speech (label 1). such a distribution results in a modest class skew, with hate speech occurrences representing approximately 38% of the data. the development set has 787 samples, 485 of which are non-hate (label 0) and 302 of hate speech (label 1). while the dev set is somewhat balanced, the imbalance in training data necessitated additional strategies to ensure fair learning between classes. to compensate for this imbalance, and especially in the training, we exploited oversampling of the hate speech class and highly aggressive data augmentation. through this, the present approach intends to expose the model to a broader set of linguistic variants related to hate speech while preserving diversification in the training examples. the use of label-aware augmentation and loss function finetuning assists the model to generalise better to minority-class cases that are crucial in real-world detection of hate speech. ## system overview we use a binary classification approach in searching for caste and migration-related hate speech in tamil-english cs posts on social media. we fine-tune the xlm-roberta-large transformer model, which is known for its powerful multilingual representation and is exceptionally efficient for low-resourced and code-mixed settings. the task entails labelling content as non-hate (0) or hate speech (1). to correct class imbalance and make better guesses on the instances of minority classes, we use focal loss instead of a regular cross-entropy with alteration of gamma and alpha parameters. on pre-processing, we clean missing values and normalize labels. to balance the dataset, we have oversampled for hate speech instances five times and augmented it with strong language-level augmentations, including synonym replacement, token dropout, and word scrambling, to diversify the use of language. text is to be tokenized by the multilingual tokenizer of the xlm-roberta-large, which would apply padding or truncation based on a max length of 128. the optimizer used to train the model is adamw with gradient clipping and accumulation. we use a learning rate of 5e-6 with reducelronplateau and early stopping on validation f1 score. in order to enhance hate speech recall, we set the threshold for classification to 0.3 while inferring. performance evaluation is done using precision, recall, weighted, and per-class f1 scores, and a confusion matrix. the best model checkpoint is auto-saved based on validation f1, also with the tokenizer and fine-tuned weights for easy deployment. our system is designed for the lt-edi@ldk 2025 task, addressing central issues of class imbalance, low-resource limitations, and code-switching, improving the detection of hate speech in challenging linguistic and social environments. ## results our approach's score when used to evaluate the tamil-english dataset used in detecting hate speech related to caste and migration was a weighted f1 score of 0.6567. with xlm-roberta-large modeled with focal loss and aggressive augmentation, the model delivered impressive generalization in a scenario where resources are constrained and data is imbalanced. the class-wise model's results implied that it could reliably distinguish hate speech, and techniques such as oversampling, synonym-based augmentation, and threshold adjustment enhanced its performance. although performance limitations, especially with respect to minority classes, are involved, the performance results provide grounds for advocating the use of class-focused learning and multilingual approaches for this task.