@inproceedings{r-etal-2025-linguaists,
title = "{L}ingu{AI}sts@{D}ravidian{L}ang{T}ech 2025: Misogyny Meme Detection using multimodel Approach",
author = "R, Arthi and
J, Pavithra and
Manikandan, Dr G and
A, Lekhashree and
G, Dhanyashree and
Sahitya, Bommineni and
K, Arivuchudar and
K, Kalpana",
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-1.54/",
doi = "10.18653/v1/2025.dravidianlangtech-1.54",
pages = "309--314",
ISBN = "979-8-89176-228-2",
abstract = "Memes often disseminate misogynistic material, which nurtures gender discrimination and stereotyping. While it is an effective tool of communication, social media has also provided a fertile ground for online abuse. This vital issue in the multilingual and multimodal setting is tackled by the Misogyny Meme Detection Shared Task. Our method employs advanced NLP techniques and machine learning models to classify memes in Malayalam and Tamil, two low-resource languages. Preprocessing of text includes tokenization, lemmatization, and stop word removal. Features are then extracted using TF-IDF. With the best achievable hyperparameters, along with the SVM model, our system provided very promising outcomes and ranked 9th among the systems competing in the Tamil task with a 0.71259 F1-score, and ranked 15th with an F1-score of 0.68186 in the Malayalam taks. With this research work, it would be established how important AI-based solutions are toward stopping online harassment and developing secure online spaces."
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<abstract>Memes often disseminate misogynistic material, which nurtures gender discrimination and stereotyping. While it is an effective tool of communication, social media has also provided a fertile ground for online abuse. This vital issue in the multilingual and multimodal setting is tackled by the Misogyny Meme Detection Shared Task. Our method employs advanced NLP techniques and machine learning models to classify memes in Malayalam and Tamil, two low-resource languages. Preprocessing of text includes tokenization, lemmatization, and stop word removal. Features are then extracted using TF-IDF. With the best achievable hyperparameters, along with the SVM model, our system provided very promising outcomes and ranked 9th among the systems competing in the Tamil task with a 0.71259 F1-score, and ranked 15th with an F1-score of 0.68186 in the Malayalam taks. With this research work, it would be established how important AI-based solutions are toward stopping online harassment and developing secure online spaces.</abstract>
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%0 Conference Proceedings
%T LinguAIsts@DravidianLangTech 2025: Misogyny Meme Detection using multimodel Approach
%A R, Arthi
%A J, Pavithra
%A Manikandan, Dr G.
%A A, Lekhashree
%A G, Dhanyashree
%A Sahitya, Bommineni
%A K, Arivuchudar
%A K, Kalpana
%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 r-etal-2025-linguaists
%X Memes often disseminate misogynistic material, which nurtures gender discrimination and stereotyping. While it is an effective tool of communication, social media has also provided a fertile ground for online abuse. This vital issue in the multilingual and multimodal setting is tackled by the Misogyny Meme Detection Shared Task. Our method employs advanced NLP techniques and machine learning models to classify memes in Malayalam and Tamil, two low-resource languages. Preprocessing of text includes tokenization, lemmatization, and stop word removal. Features are then extracted using TF-IDF. With the best achievable hyperparameters, along with the SVM model, our system provided very promising outcomes and ranked 9th among the systems competing in the Tamil task with a 0.71259 F1-score, and ranked 15th with an F1-score of 0.68186 in the Malayalam taks. With this research work, it would be established how important AI-based solutions are toward stopping online harassment and developing secure online spaces.
%R 10.18653/v1/2025.dravidianlangtech-1.54
%U https://aclanthology.org/2025.dravidianlangtech-1.54/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.54
%P 309-314
Markdown (Informal)
[LinguAIsts@DravidianLangTech 2025: Misogyny Meme Detection using multimodel Approach](https://aclanthology.org/2025.dravidianlangtech-1.54/) (R et al., DravidianLangTech 2025)
ACL
- Arthi R, Pavithra J, Dr G Manikandan, Lekhashree A, Dhanyashree G, Bommineni Sahitya, Arivuchudar K, and Kalpana K. 2025. LinguAIsts@DravidianLangTech 2025: Misogyny Meme Detection using multimodel Approach. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 309–314, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.