Hinterwelt@ LT - EDI 2025: A Transformer-Based Detection of Caste and Migration Hate Speech in T amil Social Media (2025)

Abstract: This paper presents our system for detecting caste and migration-related hate speech in Tamil social media comments, addressing the challenges in this low-resource language setting. We experimented with multiple approaches on a dataset of 7,875 annotated comments. Our methodology encompasses traditional machine learning classifiers (SVM, Random Forest, KNN), deep learning models (CNN, CNN-BiLSTM), and transformer-based architectures (MuRIL, IndicBERT, XLM-RoBERTa). Comprehensive evaluations demonstrate that transformer-based models substantially outperform traditional approaches, with MuRIL-large achieving the highest performance with a macro F1 score of 0.8092. Error analysis reveals challenges in detecting implicit and culturally-specific hate speech expressions requiring deeper socio-cultural context. Our team ranked 5th in the LT-EDI@LDK 2025 shared task with an F1 score of 0.80916. This work contributes to combating harmful online content in low-resource languages and highlights the effectiveness of large pre-trained multilingual models for nuanced text classification tasks.

## introduction the ability to communicate with anyone from any part of the globe has been enabled through social media platforms, which have optimistic advantages. though its merits are many, social media platforms have worked as a catalyst and sometimes as an efficient medium for hate speech propagation aimed at various communities, spreading derogating remarks based on caste or migration. this is a great matter of concern which deeply threatens the social unity of india. as concerning as it is, there's a clear lack of resources to tackle this problem. moreover, tackling the problem of detecting caste and * authors contributed equally to this work. migration related hate speeches in low resource languages like tamil is extremely difficult due to the lack of bounded datasets, intricate language forms, and anthropological aspects, which have their own complexities. with the intention of promoting multilingual natural language processing (nlp) along with ethical artificial intelligence (ai) gives a tamil dataset of 7875 social media comments which were previously marked as caste/migration hate speeches with non hate speeches. using exploratory data analysis (eda), we have bounced protection methods and comparative analysis structures to devise informative classifiers based on the dataset attributes which exhibit 61.8:38.2 class imbalance along with other characteristics like equal text length across all classes, balancing the classification approach. our model tries to balance the detection of hate speech using various approaches hails from the low resource and imbalanced face of the task. this particular investigation works towards creating more active digital environments by building stronger systems for tamil hate speech detection. the critical contributions of this work are: our work addresses a significant gap in hate speech detection for tamil, specifically targeting casteist and migration-related content on social media, a context largely underrepresented in existing resources. we engage with both categories in a unified setting, offering detection in dataset and evaluating modern classification strategies. while prior research has made strides in tamil hate speech detection, most efforts have focused on offensive language in general or on single categories. mohan et al. (2025) introduced a multimodal dataset for casteist content, but its limited size restricts scalability. reddy et al. (2024) used ensemble classifiers combining svm, random forest, and naive bayes, achieving promising results but highlighting challenges like ineffective pos tagging for tamil. deep learning-based approaches, like the cnn-bilstm + transformer models used by sangeetham et al. (2024), demonstrate the value of contextual embeddings but were limited by dataset scope. efforts like shahiki tash et al. (2024) focused solely on migration discourse. our approach builds on these foundations by exploring transformer-based models fine-tuned specifically for caste and migration hate speech. offers a valuable opportunity to evaluate classification models in a dual-category, low-resource setting, contributing an important benchmark for hate speech detection in tamil and related languages. looking ahead, we aim to expand dataset coverage and evaluate techniques like domain-adaptive pre-training and cross-lingual transfer from codemixed hindi-english hate speech models, contributing toward building fairer, safer online spaces for marginalized communities. ## task and dataset description we participated in the shared task on caste and migration hate speech detection at lt-edi@ldk 2025 @xcite . the goal was to automatically classify tamil social media text as either 'caste/migration-related hate speech' (label 1) or 'non-caste/migration-related hate speech' (label 0). the provided csv dataset (chakravarthi, 2020) comprised 5,512 training, 787 development, and 1,576 test instances, with a notable class imbalance favoring non-hate speech. performance was officially evaluated using the macro f1-score. ## methodology several machine learning (ml), deep learning (dl), and transformer-based models were employed to establish a robust baseline, as illustrated in figure 2 . ## conclusion in this study, we addressed the challenging task of detecting caste and migration-related hate speech in tamil social media content. we systematically evaluated a range of machine learning, deep learning, and transformer-based models. our findings indicate that transformer architectures, particularly muril-large, achieve superior performance, demonstrating the efficacy of large pre-trained multilingual models for this nuanced task. while these models show promise, error analysis reveals challenges with implicit hate and colloquialisms, suggesting avenues for future work in enhancing contextual understanding and incorporating cultural nuances to further improve detection accuracy and contribute to safer online environments.