@inproceedings{chowdhury-etal-2025-fired,
title = "{F}ired{\_}from{\_}{NLP}@{D}ravidian{L}ang{T}ech 2025: A Multimodal Approach for Detecting Misogynistic Content in {T}amil and {M}alayalam Memes",
author = "Chowdhury, Md. Sajid Alam and
Chowdhury, Mostak Mahmud and
Shanto, Anik Mahmud and
Abrar, Jidan Al and
Murad, Hasan",
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-1.81/",
doi = "10.18653/v1/2025.dravidianlangtech-1.81",
pages = "459--464",
ISBN = "979-8-89176-228-2",
abstract = "In the context of online platforms, identifying misogynistic content in memes is crucial for maintaining a safe and respectful environment. While most research has focused on high-resource languages, there is limited work on languages like Tamil and Malayalam. To address this gap, we have participated in the Misogyny Meme Detection task organized by DravidianLangTech@NAACL 2025, utilizing the provided dataset named MDMD (Misogyny Detection Meme Dataset), which consists of Tamil and Malayalam memes. In this paper, we have proposed a multimodal approach combining visual and textual features to detect misogynistic content. Through a comparative analysis of different model configurations, combining various deep learning-based CNN architectures and transformer-based models, we have developed fine-tuned multimodal models that effectively identify misogynistic memes in Tamil and Malayalam. We have achieved an F1 score of 0.678 for Tamil memes and 0.803 for Malayalam memes."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chowdhury-etal-2025-fired">
<titleInfo>
<title>Fired_from_NLP@DravidianLangTech 2025: A Multimodal Approach for Detecting Misogynistic Content in Tamil and Malayalam Memes</title>
</titleInfo>
<name type="personal">
<namePart type="given">Md.</namePart>
<namePart type="given">Sajid</namePart>
<namePart type="given">Alam</namePart>
<namePart type="family">Chowdhury</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mostak</namePart>
<namePart type="given">Mahmud</namePart>
<namePart type="family">Chowdhury</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anik</namePart>
<namePart type="given">Mahmud</namePart>
<namePart type="family">Shanto</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jidan</namePart>
<namePart type="given">Al</namePart>
<namePart type="family">Abrar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hasan</namePart>
<namePart type="family">Murad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages</title>
</titleInfo>
<name type="personal">
<namePart type="given">Bharathi</namePart>
<namePart type="given">Raja</namePart>
<namePart type="family">Chakravarthi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruba</namePart>
<namePart type="family">Priyadharshini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anand</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="family">Madasamy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sajeetha</namePart>
<namePart type="family">Thavareesan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elizabeth</namePart>
<namePart type="family">Sherly</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saranya</namePart>
<namePart type="family">Rajiakodi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Balasubramanian</namePart>
<namePart type="family">Palani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Malliga</namePart>
<namePart type="family">Subramanian</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Subalalitha</namePart>
<namePart type="family">Cn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dhivya</namePart>
<namePart type="family">Chinnappa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-228-2</identifier>
</relatedItem>
<abstract>In the context of online platforms, identifying misogynistic content in memes is crucial for maintaining a safe and respectful environment. While most research has focused on high-resource languages, there is limited work on languages like Tamil and Malayalam. To address this gap, we have participated in the Misogyny Meme Detection task organized by DravidianLangTech@NAACL 2025, utilizing the provided dataset named MDMD (Misogyny Detection Meme Dataset), which consists of Tamil and Malayalam memes. In this paper, we have proposed a multimodal approach combining visual and textual features to detect misogynistic content. Through a comparative analysis of different model configurations, combining various deep learning-based CNN architectures and transformer-based models, we have developed fine-tuned multimodal models that effectively identify misogynistic memes in Tamil and Malayalam. We have achieved an F1 score of 0.678 for Tamil memes and 0.803 for Malayalam memes.</abstract>
<identifier type="citekey">chowdhury-etal-2025-fired</identifier>
<identifier type="doi">10.18653/v1/2025.dravidianlangtech-1.81</identifier>
<location>
<url>https://aclanthology.org/2025.dravidianlangtech-1.81/</url>
</location>
<part>
<date>2025-05</date>
<extent unit="page">
<start>459</start>
<end>464</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Fired_from_NLP@DravidianLangTech 2025: A Multimodal Approach for Detecting Misogynistic Content in Tamil and Malayalam Memes
%A Chowdhury, Md. Sajid Alam
%A Chowdhury, Mostak Mahmud
%A Shanto, Anik Mahmud
%A Abrar, Jidan Al
%A Murad, Hasan
%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 chowdhury-etal-2025-fired
%X In the context of online platforms, identifying misogynistic content in memes is crucial for maintaining a safe and respectful environment. While most research has focused on high-resource languages, there is limited work on languages like Tamil and Malayalam. To address this gap, we have participated in the Misogyny Meme Detection task organized by DravidianLangTech@NAACL 2025, utilizing the provided dataset named MDMD (Misogyny Detection Meme Dataset), which consists of Tamil and Malayalam memes. In this paper, we have proposed a multimodal approach combining visual and textual features to detect misogynistic content. Through a comparative analysis of different model configurations, combining various deep learning-based CNN architectures and transformer-based models, we have developed fine-tuned multimodal models that effectively identify misogynistic memes in Tamil and Malayalam. We have achieved an F1 score of 0.678 for Tamil memes and 0.803 for Malayalam memes.
%R 10.18653/v1/2025.dravidianlangtech-1.81
%U https://aclanthology.org/2025.dravidianlangtech-1.81/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.81
%P 459-464
Markdown (Informal)
[Fired_from_NLP@DravidianLangTech 2025: A Multimodal Approach for Detecting Misogynistic Content in Tamil and Malayalam Memes](https://aclanthology.org/2025.dravidianlangtech-1.81/) (Chowdhury et al., DravidianLangTech 2025)
ACL
- Md. Sajid Alam Chowdhury, Mostak Mahmud Chowdhury, Anik Mahmud Shanto, Jidan Al Abrar, and Hasan Murad. 2025. Fired_from_NLP@DravidianLangTech 2025: A Multimodal Approach for Detecting Misogynistic Content in Tamil and Malayalam Memes. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 459–464, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.