CUET _ B litz_ A ces@ LT - EDI -2025: Leveraging Transformer Ensembles and Majority Voting for Hate Speech Detection (2025)

Abstract: The rapid growth of the internet and social media has given people an open space to share their opinions, but it has also led to a rise in hate speech targeting different social, cultural, and political groups. While much of the research on hate speech detection has focused on widely spoken languages, languages like Tamil, which are less commonly studied, still face significant gaps in this area. To tackle this, the Shared Task on Caste and Migration Hate Speech Detection was organized at the Fifth Workshop on Language Technology for Equality, Diversity, and Inclusion (LT-EDI-2025). This paper aims to create an automatic system that can detect caste and migration-related hate speech in Tamil-language social media content. We broke down our approach into two phases: in the first phase, we tested seven machine learning models and five transformer-based models. In the second phase, we combined the predictions from the fine-tuned transformers using a majority voting technique. This ensemble approach outperformed all other models, achieving the highest macro F1 score of 0.81682, which earned us 4th place in the competition.

## introduction social media is a crucial platform for accessing upto-date information while also providing a space for individuals to exchange ideas, opinions, and thoughts, fostering meaningful conversations and building connections @xcite . although this democratization of expression enables people to express viewpoints and participate in debates, it has also contributed to the emergence of a major problem: the widespread transmission of hate speech @xcite . hate speech fuels division and polarization, escalating tensions between caste and migrant groups, and triggering discrimination and violence based * these authors contributed equally to this work on race, religion, gender, migration status, or other factors, threatening societal harmony and wellbeing @xcite . it can deeply psychologically affect its victims, hence generating emotional damage like fear, anxiety, depression, and a sense of isolation @xcite . therefore, it is essential to implement automatic regulation of hateful content online to minimize the harm it can cause to society. significant study on the automatic identification of hate speech in text has been prompted by recent natural language processing (nlp) developments. events to discover better techniques for automated hate speech detection have been held in major contests including semeval-2019 @xcite , semeval-2020 @xcite , and germeval-2018 @xcite . from many sources, researchers have built large-scale datasets that have inspired more research in this field, including studies on non-english languages and other online communities. these initiatives have opened up several processing pipelines for investigation, including different feature sets, machine learning techniques (supervised, unsupervised, and semisupervised), and classification algorithms such as naive bayes, logistic regression (lr), convolutional neural networks (cnn), lstm, and bert deep learning models @xcite . our work aims to develop a system capable of distinguishing caste and migration hate speech from non-caste and migration hate speech, focusing on a low-resource language like tamil @xcite , which belongs to the dravidian language family @xcite . the primary contributions of our work are: codes are available at github repository. ## related work recent works have explored various computational approaches for automated detection of online hate speech targeting caste identities and migrant communities, ranging from traditional machine learning to advanced deep learning architectures. @xcite evaluated several models for tamil hate speech detection. their experiments showed m-bert achieved an f1-score of 0.8049, outperforming bilstm (0.7490) and tamil bert (0.7847). in the context of tamil-english code-mixed hate speech detection, (pokrywka and jassem, 2024) evaluated various transformer models, with google/muril-large-cased demonstrating strong performance with an f1score of 0.81 on the challenge test set. they also experimented with xlm-roberta-large, bert-basemultilingual-cased, and roberta-base models. taking a different approach, @xcite explored traditional machine learning techniques for abusive comment detection in tamil, reporting performance metrics for k-nearest neighbor (0.0772), decision tree (0.5862), and naive bayes (0.5905). @xcite utilized an ensemble approach based on transformer models for tamil hate speech detection, with muril cased achieving an f1-score of 0.60, while also experimenting with xlm roberta large. @xcite combined traditional machine learning approaches for tamil hate speech detection, with support vector machines (svm) performing remarkably well with an f1-score of 0.80, alongside random forest classifier (rfc) and decision tree implementations. @xcite proposed a machine learning approach for abusive comment detection in tamil, achieving a macro-f1 score of 0.35. their research revealed an important insight: traditional machine learning models can sometimes outperform sophisticated deep learning techniques when datasets are limited in size and complexity. deep learning approaches tailored for code-mixed environments have shown promising results. @xcite ) employed a synonym-based bi-lstm model for tamil-english code-mixed hate speech detection, achieving an f1 score of 0.8169. their model demonstrated particular effectiveness in distinguishing between hate (f1 score: 0.8110) and non-hate texts (f1 score: 0.8050). @xcite evaluated traditional machine learning models against transfer learning approaches for offensive language detection in tamil youtube comments. ## task and dataset description the shared task on caste and migration hate speech detection is part of lt-edi@ldk 2025 foot_0 . in this task, we focused mainly on tamil-language content and aimed to build an automatic classification model that can analyze text from social media platforms and determine whether it contains caste-based or migration-related hate speech. the dataset was provided by the organizers of the competition @xcite . the training and development datasets consist of three columns: id (unique identifier), text (comment content), and label (1 for caste/immigration hate speech, 0 for non-hate speech). the test dataset includes only id and text. figure 1 displays some samples of the training dataset. ## system overview our approach included two stages. figure 2 provides an overview of the first stage, which combines the application of various machine learning algorithms and the fine-tuning of transformers. in the second stage, we employed an ensemble transformer technique, combining the fine-tuned models with majority voting, which outperformed all other evaluated models. ## conclusion in this task, we created an automatic model to detect caste and migration-related hate speech in tamil-language content on social media. by combining traditional machine learning techniques with transformer models, our ensemble approach achieved impressive results with a precision of 0.82492, recall of 0.81139, and an f1-score of 0.81682, outperforming all other models. looking ahead, we plan to improve the model by expanding the dataset and incorporating multimodal data like images and emojis, which are common in social media posts. we also aim to explore more advanced transformer-based models for better context understanding, handling of informal language and classifying implicit hate speech. lastly, implementing real-time detection for social media could make the model even more effective in addressing hate speech as it emerges