@inproceedings{ponnusamy-etal-2026-overview,
title = "Overview of the Multimodal Homophobia and Transphobia Meme Classification Shared Task",
author = "Ponnusamy, Kishore Kumar and
Chakravarthi, Bharathi Raja and
Susaladi, Mahesh and
Ren, Junru and
Kumaresan, Prasanna Kumar and
B, Premjith and
Thenmozhi, Durairaj and
Priyadharshini, Ruba and
Chinnaudayar Navaneethakrishnan, Subalalitha",
editor = "Chakravarthi, Bharathi Raja and
B, Bharathi and
Durairaj, Thenmozhi and
Garc{\'i}a Cumbreras, Miguel {\'A}ngel and
Jim{\'e}nez-Zafra, Salud Mar{\'i}a",
booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = jul,
year = "2026",
address = "Virtual (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.ltedi-1.13/",
doi = "10.18653/v1/2026.ltedi-1.13",
pages = "141--149",
ISBN = "979-8-89176-424-8",
abstract = "This paper presents an overview of the Shared Task on detecting homophobia and transphobia in meme datasets across three languages: Hindi, English, and Chinese. With the rapid growth of internet users worldwide, memes have become a widely used medium for expressing humor, satire, and sarcasm on social media platforms. However, their increasing popularity has also facilitated the spread of hate, misinformation, and propaganda targeting specific communities. Hateful memes often attack individuals or groups based on attributes such as physical appearance, language, ethnicity, religion, or sexual orientation. Among those affected, the LGBTQ+ community is particularly vulnerable and frequently targeted on social media platforms. To address this issue, we organized a shared task that focuses on identifying homophobic and transphobic hate in memes. The task aims to encourage the development of automated systems capable of detecting such harmful content across multiple languages. Evaluation was conducted using Macro F1-score as the primary metric. The top performing system achieved a Macro F1-score of 0.8377 for English, 0.8081 for Hindi, and 0.7535 for Chinese, demonstrating promising results for multilingual hate detection in memes."
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<abstract>This paper presents an overview of the Shared Task on detecting homophobia and transphobia in meme datasets across three languages: Hindi, English, and Chinese. With the rapid growth of internet users worldwide, memes have become a widely used medium for expressing humor, satire, and sarcasm on social media platforms. However, their increasing popularity has also facilitated the spread of hate, misinformation, and propaganda targeting specific communities. Hateful memes often attack individuals or groups based on attributes such as physical appearance, language, ethnicity, religion, or sexual orientation. Among those affected, the LGBTQ+ community is particularly vulnerable and frequently targeted on social media platforms. To address this issue, we organized a shared task that focuses on identifying homophobic and transphobic hate in memes. The task aims to encourage the development of automated systems capable of detecting such harmful content across multiple languages. Evaluation was conducted using Macro F1-score as the primary metric. The top performing system achieved a Macro F1-score of 0.8377 for English, 0.8081 for Hindi, and 0.7535 for Chinese, demonstrating promising results for multilingual hate detection in memes.</abstract>
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%0 Conference Proceedings
%T Overview of the Multimodal Homophobia and Transphobia Meme Classification Shared Task
%A Ponnusamy, Kishore Kumar
%A Chakravarthi, Bharathi Raja
%A Susaladi, Mahesh
%A Ren, Junru
%A Kumaresan, Prasanna Kumar
%A B, Premjith
%A Thenmozhi, Durairaj
%A Priyadharshini, Ruba
%A Chinnaudayar Navaneethakrishnan, Subalalitha
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Durairaj, Thenmozhi
%Y García Cumbreras, Miguel Ángel
%Y Jiménez-Zafra, Salud María
%S Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2026
%8 July
%I Association for Computational Linguistics
%C Virtual (Online)
%@ 979-8-89176-424-8
%F ponnusamy-etal-2026-overview
%X This paper presents an overview of the Shared Task on detecting homophobia and transphobia in meme datasets across three languages: Hindi, English, and Chinese. With the rapid growth of internet users worldwide, memes have become a widely used medium for expressing humor, satire, and sarcasm on social media platforms. However, their increasing popularity has also facilitated the spread of hate, misinformation, and propaganda targeting specific communities. Hateful memes often attack individuals or groups based on attributes such as physical appearance, language, ethnicity, religion, or sexual orientation. Among those affected, the LGBTQ+ community is particularly vulnerable and frequently targeted on social media platforms. To address this issue, we organized a shared task that focuses on identifying homophobic and transphobic hate in memes. The task aims to encourage the development of automated systems capable of detecting such harmful content across multiple languages. Evaluation was conducted using Macro F1-score as the primary metric. The top performing system achieved a Macro F1-score of 0.8377 for English, 0.8081 for Hindi, and 0.7535 for Chinese, demonstrating promising results for multilingual hate detection in memes.
%R 10.18653/v1/2026.ltedi-1.13
%U https://aclanthology.org/2026.ltedi-1.13/
%U https://doi.org/10.18653/v1/2026.ltedi-1.13
%P 141-149
Markdown (Informal)
[Overview of the Multimodal Homophobia and Transphobia Meme Classification Shared Task](https://aclanthology.org/2026.ltedi-1.13/) (Ponnusamy et al., LTEDI 2026)
ACL
- Kishore Kumar Ponnusamy, Bharathi Raja Chakravarthi, Mahesh Susaladi, Junru Ren, Prasanna Kumar Kumaresan, Premjith B, Durairaj Thenmozhi, Ruba Priyadharshini, and Subalalitha Chinnaudayar Navaneethakrishnan. 2026. Overview of the Multimodal Homophobia and Transphobia Meme Classification Shared Task. In Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 141–149, Virtual (Online). Association for Computational Linguistics.