girlsteam@ LT - EDI -2025: Caste/Migration based hate speech Detection (2025)

Abstract: The proliferation of caste- and migration-based hate speech on social media poses a significant challenge, particularly in low-resource languages like Tamil. This paper presents our approach to the LT-EDI@ACL 2025 shared task, addressing this issue through a hybrid transformer-based framework. We explore a range of Machine Learning (ML), Deep Learning (DL), and multilingual transformer models, culminating in a novel m-BERT+BiLSTM hybrid architecture. This model integrates contextual embeddings from m-BERT with lexical features from TF-IDF and FastText, feeding the enriched representations into a BiLSTM to capture bidirectional semantic dependencies. Empirical results demonstrate the superiority of this hybrid architecture, achieving a macro-F1 score of 0.76 on the test set and surpassing the performance of standalone models such as MuRIL and IndicBERT. These results affirm the effectiveness of hybrid multilingual models for hate speech detection in low-resource and culturally complex linguistic settings.

## introduction hate speech encompasses expressions-verbal, written, or behavioral-that incite hostility or dehumanize individuals based on identity markers such as race, caste, gender, religion, or migration status. it reinforces prejudice and systemic discrimination, undermining individual dignity and social cohesion. caste-and migration-based hate speech, in particular, reflects deep-rooted structural inequalities conveyed through derogatory narratives @xcite . the rise of social media has amplified such harmful content, especially against marginalized communities in multilingual countries like india @xcite . in this context, hate speech frequently appears in tamil, english, and tanglish-a codemixed variant of tamil in latin script-posing unique challenges for automated detection. tamil, a classical dravidian language spoken by over 70 million people @xcite , presents significant challenges for natural language processing (nlp) due to its complex morphology and limited annotated resources. their workshop paper provided us an opportunity to engage with these challenges in processing mixedup languages and to leverage our work @xcite . recent advances in multilingual nlp have produced models tailored to indian languages, such as indicbert, muril, and mbert @xcite . these transformer-based models, alongside machine learning (ml) and deep learning (dl) methods, form a robust foundation for tackling hate speech detection in such contexts. for the lt-edi 2025 shared task, we propose a comprehensive system for identifying caste-and migration-related hate speech in tamil social media. our main objective was-• to develop a robust multilingual system for detecting caste and migration related hate speech in tamil social media by leveraging tamil, english, and code-mixed (tanglish) text. our code, developed for this shared task can be accessed at 1 2 related work to address the complexities of caste and migration hate speech in tamil, we propose a hybrid mbert+bilstm model. this architecture combines the contextual understanding of mbert with the sequential learning of bilstm to improve the detection accuracy in this underexplored domain. ## task and dataset description this shared task focuses on caste and migration hate speech detection. the objective of the task is to develop automatic classification models that can analyze social media texts, with a specific emphasis on content related to caste and migration. the task organizers provided a dataset containing social media posts in a mix of languages: the dataset comprises text in various language forms, including english (e.g., "mumbai bangalore la 80 percentage outsiders"), code-mixed english-tamil, tanglish (tamil written in the latin script), and pure tamil. each entry in the dataset consists of the following: the classification labels are defined as follows: the data set was segmented into training, development, and test data subsets to help with the thorough analysis and to facilitate model training. the class distribution is summarized in ## result in this section, we compare the performance of various machine learning (ml) and deep learning (dl) models. the effectiveness of each model is primarily evaluated using the macro f1-score. the hyperparameters for the dl models were manually fine-tuned based on their performance on the validation dataset. the sum of the precision (p), recall (r) and macro-f1 (mf1) scores for each model in the test set is presented in the ## error analysis 6.1 quantitative discussion to evaluate model performance, both quantitative and qualitative analyses were conducted. the confusion matrix figure 2 showed 395 true negatives, 211 true positives, 90 false positives, and 91 false negatives, indicating a slight bias toward the negative class. qualitatively, the mbert+bilstm model effectively detected explicit caste-and migration-related hate speech in tamil-english code-mixed text but struggled with satirical or metaphorical expressions. errors were often due to class imbalance and subtle language use, highlighting the need for sociocultural and discourse-level understanding. 7 conclusion and future work