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        <title>CUET_Blitz_Aces@LT-EDI-2025: Leveraging Transformer Ensembles and Majority Voting for Hate Speech Detection</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T CUET_Blitz_Aces@LT-EDI-2025: Leveraging Transformer Ensembles and Majority Voting for Hate Speech Detection
%A Karim, Shahriar Farhan
%A Kashmary, Anower Sha Shajalal
%A Murad, Hasan
%Y Gkirtzou, Katerina
%Y Žitnik, Slavko
%Y Gracia, Jorge
%Y Gromann, Dagmar
%Y di Buono, Maria Pia
%Y Monti, Johanna
%Y Ionov, Maxim
%S Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2025
%8 September
%I Unior Press
%C Naples, Italy
%@ 978-88-6719-334-9
%F karim-etal-2025-cuet
%X 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.
%U https://aclanthology.org/2025.ltedi-1.23/
%P 133-139

Markdown (Informal)

[CUET_Blitz_Aces@LT-EDI-2025: Leveraging Transformer Ensembles and Majority Voting for Hate Speech Detection](https://aclanthology.org/2025.ltedi-1.23/) (Karim et al., LTEDI 2025)

• CUET_Blitz_Aces@LT-EDI-2025: Leveraging Transformer Ensembles and Majority Voting for Hate Speech Detection (Karim et al., LTEDI 2025)

ACL

• Shahriar Farhan Karim, Anower Sha Shajalal Kashmary, and Hasan Murad. 2025. CUET_Blitz_Aces@LT-EDI-2025: Leveraging Transformer Ensembles and Majority Voting for Hate Speech Detection. In Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 133–139, Naples, Italy. Unior Press.