NS @ LT - EDI -2025 C aste M igration based hate speech Detection (2025)

Abstract: Hate speech directed at caste and migrant communities is a widespread problem on social media, frequently taking the form of insults specific to a given region, coded language, and disparaging slurs. This type of abuse seriously jeopardizes both individual well-being and social harmony in addition to perpetuating discrimination. In order to promote safer and more inclusive digital environments, it is imperative that this challenge be addressed. However, linguistic subtleties, code-mixing, and the lack of extensive annotated datasets make it difficult to detect such hate speech in Indian languages like Tamil. We suggest a supervised machine learning system that uses FastText embeddings specifically designed for Tamil-language content and Whisper-based speech recognition to address these issues. This strategy aims to precisely identify hate speech connected to caste and migration, supporting the larger endeavor to reduce online abuse in low resource languages like Tamil.

## introduction the rapid and sudden expansion of social media websites has transformed and revolutionized how people communicate with each other, exchange different types of information, and interact with a vast spectrum of disparate communities and communities of interest across the world @xcite . nevertheless, it also needs to be observed that such cyberspaces have also turned into important hotspots and breeding grounds for spreading hate speech, with the focus being specifically placed on targeting vulnerable communities, i.e., marginalized caste groups and migrant communities @xcite . this malicious kind of abuse usually tends to manifest itself in the form of region-based abuses, coded language imparting veiled messages, and a range of derogatory statements that are employed to maintain prevailing social hierarchies and legitimize systemic discrimination @xcite . the negative effects and negative implications stemming from such toxic content extend far and beyond the specific harm that results, and pose a very realistic threat to social cohesion, community harmony, and overall inclusivity in society @xcite . although a great deal of research has been conducted and a great deal of investments have been made towards hate speech detection in highresource languages like english, much remains to be done in addressing this very serious problem in low-resource languages properly @xcite @xcite . this is a very acute problem in linguistically diverse and rich countries like india, where there are numerous languages and dialects spoken @xcite . tamil, being one of the major languages of south india, has very distinctive problems concerning this, mainly because of its very unique linguistic properties, difference among various dialects, and the widespread use of code-mixing by the speakers. these cumulative problems make it particularly challenging to create useful automated hate speech detection systems. moreover, the unavailability of properly annotated datasets only serves to further add to the issues of creating useful detection systems for this kind of abusive language @xcite . in an effort to efficiently solve and address these major challenges, the shared task on caste and migration hate speech detection, to be hosted at the well-regarded lt-edi@ldk 2025 conference, has been crafted with the specific goal of encouraging and enabling large-scale research and development of robust machine learning models for addressing this major and urgent issue. the new proposed solution exploits fasttext-based embeddings that have been specifically designed for tamil text processing and analysis. with a robust emphasis on hate speech detection and solving based on caste and migration challenges, this major task not only progresses but also fits within the wider and more general goal of creating safer, more respectful, and more inclusive digital spaces. this is especially critical for communities that use low-resource languages, which are likely to be exposed to special challenges and vulnerabilities in the digital space. more details about the shared task can be found at 1 . ## dataset the dataset was distributed by the shared task organisers of caste and migration hate speech detection -lt-edi@ldk 2025 @xcite . the dataset used for this study comprises sentences in the tamil language, categorized into two classes: abusive and non-abusive (class distribution). the data set is split into training and test sets. it was specifically developed for evaluating the suitability of language models for identifying abusive language in low-resource dravidian languages, ensuring near-balanced abusive and non-abusive example representations to provide more efficient training and evaluation. ## evaluation the model is trained using the training embeddings and evaluated using the test embeddings. ## limitations while working on this topic, the major limitation we faced is the mixed language, as some text was in english and some was in tamil. translating one language causes loss of contextual information which in this case is important even though it provided additional features that can help ml classifiers help classify better.