Findings of the Shared Task Caste and Migration Hate Speech Detection (2025)

Abstract: Hate speech targeting caste and migration communities is a growing concern in online platforms, particularly in linguistically diverse regions. By focusing on Tamil language text content, this task provides a unique opportunity to tackle caste or migration related hate speech detection in a low resource language Tamil, contributing to a safer digital space. We present the results and main findings of the shared task caste and migration hate speech detection. The task is a binary classification determining whether a text is caste/migration related hate speech or not. The task attracted 17 participating teams, experimenting with a wide range of methodologies from traditional machine learning to advanced multilingual transformers. The top performing system achieved a macro F1-score of 0.88105, enhancing an ensemble of fine-tuned transformer models including XLM-R and MuRIL. Our analysis highlights the effectiveness of multilingual transformers in low resource, ensemble learning, and culturally informed socio political context based techniques.

## introduction in the current digital world, the online social network platforms such as twitter, youtube, linkedin, facebook and whatsapp are widely used by the individuals from the different parts of the country @xcite . due to the high-speed internet facility, the news like an offensive speech @xcite , fake news @xcite , hate speech and its curated forms against the caste or people are disseminated across the globe in a fraction of minutes @xcite . hence, it leads to lot of issues such as protests, inflation in the economy of the country and majorly affects the healthy environment in the society @xcite . the hate speech and offensive words are more important to eradicate from the society to preserve the equality among the citizens. apart from the global issues, the discrimination and bias against the certain communities and its people will affect directly their communities and their people in terms of their mental health @xcite . hence, the hate speech against any caste/migration is an important issue and it should be detected to save the society and nation's peace. india is a democratic country and follows the "unity in diversity". according to the constitution of india foot_0 , articles 14-18 deal with the right to equality. hence if any people discriminate others in terms of language, caste or any variants, the government will take immediate actions against them with these articles. with the proliferation of social media platforms, public discourse has become increasingly dynamic, often marked by a surge of user-generated content in response to specific events @xcite . this environment has made the manual identification of caste-based hate speech both time-consuming and resource-intensive. to address this challenge, there has been a growing adoption of natural language processing (nlp) based systems capable of automatically detecting hate speech across multiple languages and platforms, thereby enhancing the efficiency and scalability of monitoring efforts @xcite . researchers are developed various machine learning (ml) and deep learning (dl) model to identify offensive language and hate speech from the various regional low-resource languages such as hindi @xcite , @xcite . a novel dataset which comprises of hate speech by castes and migration collected from youtube and published in lt-edi@eacl 2024 @xcite . in this shared task, the team of participants have developed an automatic hate speech detection against the caste and migration model. specifically, the top ranking teams used an ensemble model which consists of transformer based multilingual models such as xlm-roeerta and muril for contextual embedding of sentences to predict hate speech that achieves higher macro f1 score among other models. the work by @xcite developed six different ml models and three dl models including bi-lstm, attention, bi-lstm-cnn to identify the hate or not-hate speech. finally, they designed transformer-based models such as m-bert, xlm-r and tamil-bert for effective contextual word embedding, which achieves better performance than the other models. in the subsequent sections, we have discussed the task description, dataset statistics, methodologies used by the participants to detect caste/migration hate speech in tamil, and their results and ranking. ## results the evaluation metric used was the macro-averaged f1-score, calculated using the scikit-learn library foot_1 . ## conclusion this shared task on caste and migration hate speech detection focused on detecting hate speech against people based on their caste or migration status, especially in the tamil language. since this type of hate is often ignored in existing ai systems, this task helped researchers and developers create models that are more aware of these social issues. participants used many different methods, including machine learning and advanced models like bert and xlm-r, to analyze the text. the best teams used a mix of good text cleaning, careful model training, and techniques like ensemble voting and data balancing to get better results. the top system scored a high macro f1 of 0.88105, showing that with the right tools and approaches, it is possible to detect even subtle and hidden forms of hate speech. this task showed that we need models that understand regional languages and cultural context to handle online hate more effectively. it also highlights the need for more work in areas like explaining model predictions, creating respectful counter-speech, and labeling more detailed types of hate.