@inproceedings{g-etal-2025-overview,
title = "Overview of the Shared Task on Multimodal Hate Speech Detection in {D}ravidian languages: {D}ravidian{L}ang{T}ech@{NAACL} 2025",
author = "G, Jyothish Lal and
B, Premjith and
Chakravarthi, Bharathi Raja and
Rajiakodi, Saranya and
B, Bharathi and
Natarajan, Rajeswari and
Rajalakshmi, Ratnavel",
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-1.20/",
doi = "10.18653/v1/2025.dravidianlangtech-1.20",
pages = "114--122",
ISBN = "979-8-89176-228-2",
abstract = "The detection of hate speech in social media platforms is very crucial these days. This is due to its adverse impact on mental health, social harmony, and online safety. This paper presents the overview of the shared task on Multimodal Hate Speech Detection in Dravidian Languages organized as part of DravidianLangTech@NAACL 2025. The task emphasizes detecting hate speech in social media content that combines speech and text. Here, we focus on three low-resource Dravidian languages: Malayalam, Tamil, and Telugu. Participants were required to classify hate speech in three sub-tasks, each corresponding to one of these languages. The dataset was curated by collecting speech and corresponding text from YouTube videos. Various machine learning and deep learning-based models, including transformer-based architectures and multimodal frameworks, were employed by the participants. The submissions were evaluated using the macro F1 score. Experimental results underline the potential of multimodal approaches in advancing hate speech detection for low-resource languages. Team SSNTrio achieved the highest F1 score in Malayalam and Tamil of 0.7511 and 0.7332, respectively. Team lowes scored the best F1 score of 0.3817 in the Telugu sub-task."
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<abstract>The detection of hate speech in social media platforms is very crucial these days. This is due to its adverse impact on mental health, social harmony, and online safety. This paper presents the overview of the shared task on Multimodal Hate Speech Detection in Dravidian Languages organized as part of DravidianLangTech@NAACL 2025. The task emphasizes detecting hate speech in social media content that combines speech and text. Here, we focus on three low-resource Dravidian languages: Malayalam, Tamil, and Telugu. Participants were required to classify hate speech in three sub-tasks, each corresponding to one of these languages. The dataset was curated by collecting speech and corresponding text from YouTube videos. Various machine learning and deep learning-based models, including transformer-based architectures and multimodal frameworks, were employed by the participants. The submissions were evaluated using the macro F1 score. Experimental results underline the potential of multimodal approaches in advancing hate speech detection for low-resource languages. Team SSNTrio achieved the highest F1 score in Malayalam and Tamil of 0.7511 and 0.7332, respectively. Team lowes scored the best F1 score of 0.3817 in the Telugu sub-task.</abstract>
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%0 Conference Proceedings
%T Overview of the Shared Task on Multimodal Hate Speech Detection in Dravidian languages: DravidianLangTech@NAACL 2025
%A G, Jyothish Lal
%A B, Premjith
%A Chakravarthi, Bharathi Raja
%A Rajiakodi, Saranya
%A B, Bharathi
%A Natarajan, Rajeswari
%A Rajalakshmi, Ratnavel
%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 g-etal-2025-overview
%X The detection of hate speech in social media platforms is very crucial these days. This is due to its adverse impact on mental health, social harmony, and online safety. This paper presents the overview of the shared task on Multimodal Hate Speech Detection in Dravidian Languages organized as part of DravidianLangTech@NAACL 2025. The task emphasizes detecting hate speech in social media content that combines speech and text. Here, we focus on three low-resource Dravidian languages: Malayalam, Tamil, and Telugu. Participants were required to classify hate speech in three sub-tasks, each corresponding to one of these languages. The dataset was curated by collecting speech and corresponding text from YouTube videos. Various machine learning and deep learning-based models, including transformer-based architectures and multimodal frameworks, were employed by the participants. The submissions were evaluated using the macro F1 score. Experimental results underline the potential of multimodal approaches in advancing hate speech detection for low-resource languages. Team SSNTrio achieved the highest F1 score in Malayalam and Tamil of 0.7511 and 0.7332, respectively. Team lowes scored the best F1 score of 0.3817 in the Telugu sub-task.
%R 10.18653/v1/2025.dravidianlangtech-1.20
%U https://aclanthology.org/2025.dravidianlangtech-1.20/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.20
%P 114-122
Markdown (Informal)
[Overview of the Shared Task on Multimodal Hate Speech Detection in Dravidian languages: DravidianLangTech@NAACL 2025](https://aclanthology.org/2025.dravidianlangtech-1.20/) (G et al., DravidianLangTech 2025)
ACL
- Jyothish Lal G, Premjith B, Bharathi Raja Chakravarthi, Saranya Rajiakodi, Bharathi B, Rajeswari Natarajan, and Ratnavel Rajalakshmi. 2025. Overview of the Shared Task on Multimodal Hate Speech Detection in Dravidian languages: DravidianLangTech@NAACL 2025. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 114–122, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.