@inproceedings{ponnusamy-etal-2024-laughter-inequality,
title = "From Laughter to Inequality: Annotated Dataset for Misogyny Detection in {T}amil and {M}alayalam Memes",
author = "Ponnusamy, Rahul and
Pannerselvam, Kathiravan and
R, Saranya and
Kumaresan, Prasanna Kumar and
Thavareesan, Sajeetha and
S, Bhuvaneswari and
K.a, Anshid and
Kumar, Susminu S and
Buitelaar, Paul and
Chakravarthi, Bharathi Raja",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.660",
pages = "7480--7488",
abstract = "In this digital era, memes have become a prevalent online expression, humor, sarcasm, and social commentary. However, beneath their surface lies concerning issues such as the propagation of misogyny, gender-based bias, and harmful stereotypes. To overcome these issues, we introduced MDMD (Misogyny Detection Meme Dataset) in this paper. This article focuses on creating an annotated dataset with detailed annotation guidelines to delve into online misogyny within the Tamil and Malayalam-speaking communities. Through analyzing memes, we uncover the intricate world of gender bias and stereotypes in these communities, shedding light on their manifestations and impact. This dataset, along with its comprehensive annotation guidelines, is a valuable resource for understanding the prevalence, origins, and manifestations of misogyny in various contexts, aiding researchers, policymakers, and organizations in developing effective strategies to combat gender-based discrimination and promote equality and inclusivity. It enables a deeper understanding of the issue and provides insights that can inform strategies for cultivating a more equitable and secure online environment. This work represents a crucial step in raising awareness and addressing gender-based discrimination in the digital space.",
}
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%0 Conference Proceedings
%T From Laughter to Inequality: Annotated Dataset for Misogyny Detection in Tamil and Malayalam Memes
%A Ponnusamy, Rahul
%A Pannerselvam, Kathiravan
%A R, Saranya
%A Kumaresan, Prasanna Kumar
%A Thavareesan, Sajeetha
%A S, Bhuvaneswari
%A K.a, Anshid
%A Kumar, Susminu S.
%A Buitelaar, Paul
%A Chakravarthi, Bharathi Raja
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F ponnusamy-etal-2024-laughter-inequality
%X In this digital era, memes have become a prevalent online expression, humor, sarcasm, and social commentary. However, beneath their surface lies concerning issues such as the propagation of misogyny, gender-based bias, and harmful stereotypes. To overcome these issues, we introduced MDMD (Misogyny Detection Meme Dataset) in this paper. This article focuses on creating an annotated dataset with detailed annotation guidelines to delve into online misogyny within the Tamil and Malayalam-speaking communities. Through analyzing memes, we uncover the intricate world of gender bias and stereotypes in these communities, shedding light on their manifestations and impact. This dataset, along with its comprehensive annotation guidelines, is a valuable resource for understanding the prevalence, origins, and manifestations of misogyny in various contexts, aiding researchers, policymakers, and organizations in developing effective strategies to combat gender-based discrimination and promote equality and inclusivity. It enables a deeper understanding of the issue and provides insights that can inform strategies for cultivating a more equitable and secure online environment. This work represents a crucial step in raising awareness and addressing gender-based discrimination in the digital space.
%U https://aclanthology.org/2024.lrec-main.660
%P 7480-7488
Markdown (Informal)
[From Laughter to Inequality: Annotated Dataset for Misogyny Detection in Tamil and Malayalam Memes](https://aclanthology.org/2024.lrec-main.660) (Ponnusamy et al., LREC-COLING 2024)
ACL
- Rahul Ponnusamy, Kathiravan Pannerselvam, Saranya R, Prasanna Kumar Kumaresan, Sajeetha Thavareesan, Bhuvaneswari S, Anshid K.a, Susminu S Kumar, Paul Buitelaar, and Bharathi Raja Chakravarthi. 2024. From Laughter to Inequality: Annotated Dataset for Misogyny Detection in Tamil and Malayalam Memes. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7480–7488, Torino, Italia. ELRA and ICCL.