@inproceedings{shanmugavadivel-etal-2025-deathly-hallows,
title = "{T}he{\_}{D}eathly{\_}{H}allows@{D}ravidian{L}ang{T}ech 2025: Multimodal Hate Speech Detection in {D}ravidian Languages",
author = "Shanmugavadivel, Kogilavani and
Subramanian, Malliga and
K, Vasantharan and
A, Prethish G and
S, Santhosh",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.113/",
doi = "10.18653/v1/2025.dravidianlangtech-1.113",
pages = "661--665",
ISBN = "979-8-89176-228-2",
abstract = "The DravidianLangTech@NAACL 2025 shared task focused on multimodal hate speech detection in Tamil, Telugu, and Malayalam using social media text and audio. Our approach integrated advanced preprocessing, feature extraction, and deep learning models. For text, preprocessing steps included normalization, tokenization, stopword removal, and data augmentation. Feature extraction was performed using TF-IDF, Count Vectorizer, BERT-base-multilingual-cased, XLM-Roberta-Base, and XLM-Roberta-Large, with the latter achieving the best performance. The models attained training accuracies of 83{\%} (Tamil), 88{\%} (Telugu), and 85{\%} (Malayalam). For audio, Mel Frequency Cepstral Coefficients (MFCCs) were extracted and enhanced with augmentation techniques such as noise addition, time-stretching, and pitch-shifting. A CNN-based model achieved training accuracies of 88{\%} (Tamil), 88{\%} (Telugu), and 93{\%} (Malayalam). Macro F1 scores ranked Tamil 3rd (0.6438), Telugu 15th (0.1559), and Malayalam 12th (0.3016). Our study highlights the effectiveness of text-audio fusion in hate speech detection and underscores the importance of preprocessing, multimodal techniques, and feature augmentation in addressing hate speech on social media."
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%0 Conference Proceedings
%T The_Deathly_Hallows@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages
%A Shanmugavadivel, Kogilavani
%A Subramanian, Malliga
%A K, Vasantharan
%A A, Prethish G.
%A S, Santhosh
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F shanmugavadivel-etal-2025-deathly-hallows
%X The DravidianLangTech@NAACL 2025 shared task focused on multimodal hate speech detection in Tamil, Telugu, and Malayalam using social media text and audio. Our approach integrated advanced preprocessing, feature extraction, and deep learning models. For text, preprocessing steps included normalization, tokenization, stopword removal, and data augmentation. Feature extraction was performed using TF-IDF, Count Vectorizer, BERT-base-multilingual-cased, XLM-Roberta-Base, and XLM-Roberta-Large, with the latter achieving the best performance. The models attained training accuracies of 83% (Tamil), 88% (Telugu), and 85% (Malayalam). For audio, Mel Frequency Cepstral Coefficients (MFCCs) were extracted and enhanced with augmentation techniques such as noise addition, time-stretching, and pitch-shifting. A CNN-based model achieved training accuracies of 88% (Tamil), 88% (Telugu), and 93% (Malayalam). Macro F1 scores ranked Tamil 3rd (0.6438), Telugu 15th (0.1559), and Malayalam 12th (0.3016). Our study highlights the effectiveness of text-audio fusion in hate speech detection and underscores the importance of preprocessing, multimodal techniques, and feature augmentation in addressing hate speech on social media.
%R 10.18653/v1/2025.dravidianlangtech-1.113
%U https://aclanthology.org/2025.dravidianlangtech-1.113/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.113
%P 661-665
Markdown (Informal)
[The_Deathly_Hallows@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages](https://aclanthology.org/2025.dravidianlangtech-1.113/) (Shanmugavadivel et al., DravidianLangTech 2025)
ACL
- Kogilavani Shanmugavadivel, Malliga Subramanian, Vasantharan K, Prethish G A, and Santhosh S. 2025. The_Deathly_Hallows@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 661–665, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.