@inproceedings{subramanian-etal-2025-keclinguaists,
title = "{KECL}ingu{AI}sts@{D}ravidian{L}ang{T}ech 2025: Detecting {AI}-generated Product Reviews in {D}ravidian Languages",
author = "Subramanian, Malliga and
R, Rojitha and
Y, Mithun Chakravarthy and
V, Renusri R and
Shanmugavadivel, Kogilavani",
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-1.32/",
doi = "10.18653/v1/2025.dravidianlangtech-1.32",
pages = "187--190",
ISBN = "979-8-89176-228-2",
abstract = "With the surge of AI-generated content in online spaces, ensuring the authenticity of product reviews has become a critical challenge. This paper addresses the task of detecting AI-generated product reviews in Dravidian languages, specifically Tamil and Malayalam, which present unique hurdles due to their complex morphology, rich syntactic structures, and code-mixed nature. We introduce a novel methodology combining machine learning classifiers with advanced multilingual transformer models to identify AI-generated reviews. Our approach not only accounts for the linguistic intricacies of these languages but also leverages domain specific datasets to improve detection accuracy. For Tamil, we evaluate Logistic Regression, Random Forest, and XGBoost, while for Malayalam, we explore Logistic Regression, Multinomial Naive Bayes (MNB), and Support Vector Machines (SVM). Transformer based models significantly outperform these traditional classifiers, demonstrating superior performance across multiple metrics."
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<abstract>With the surge of AI-generated content in online spaces, ensuring the authenticity of product reviews has become a critical challenge. This paper addresses the task of detecting AI-generated product reviews in Dravidian languages, specifically Tamil and Malayalam, which present unique hurdles due to their complex morphology, rich syntactic structures, and code-mixed nature. We introduce a novel methodology combining machine learning classifiers with advanced multilingual transformer models to identify AI-generated reviews. Our approach not only accounts for the linguistic intricacies of these languages but also leverages domain specific datasets to improve detection accuracy. For Tamil, we evaluate Logistic Regression, Random Forest, and XGBoost, while for Malayalam, we explore Logistic Regression, Multinomial Naive Bayes (MNB), and Support Vector Machines (SVM). Transformer based models significantly outperform these traditional classifiers, demonstrating superior performance across multiple metrics.</abstract>
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%0 Conference Proceedings
%T KECLinguAIsts@DravidianLangTech 2025: Detecting AI-generated Product Reviews in Dravidian Languages
%A Subramanian, Malliga
%A R, Rojitha
%A Y, Mithun Chakravarthy
%A V, Renusri R.
%A Shanmugavadivel, Kogilavani
%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 subramanian-etal-2025-keclinguaists
%X With the surge of AI-generated content in online spaces, ensuring the authenticity of product reviews has become a critical challenge. This paper addresses the task of detecting AI-generated product reviews in Dravidian languages, specifically Tamil and Malayalam, which present unique hurdles due to their complex morphology, rich syntactic structures, and code-mixed nature. We introduce a novel methodology combining machine learning classifiers with advanced multilingual transformer models to identify AI-generated reviews. Our approach not only accounts for the linguistic intricacies of these languages but also leverages domain specific datasets to improve detection accuracy. For Tamil, we evaluate Logistic Regression, Random Forest, and XGBoost, while for Malayalam, we explore Logistic Regression, Multinomial Naive Bayes (MNB), and Support Vector Machines (SVM). Transformer based models significantly outperform these traditional classifiers, demonstrating superior performance across multiple metrics.
%R 10.18653/v1/2025.dravidianlangtech-1.32
%U https://aclanthology.org/2025.dravidianlangtech-1.32/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.32
%P 187-190
Markdown (Informal)
[KECLinguAIsts@DravidianLangTech 2025: Detecting AI-generated Product Reviews in Dravidian Languages](https://aclanthology.org/2025.dravidianlangtech-1.32/) (Subramanian et al., DravidianLangTech 2025)
ACL
- Malliga Subramanian, Rojitha R, Mithun Chakravarthy Y, Renusri R V, and Kogilavani Shanmugavadivel. 2025. KECLinguAIsts@DravidianLangTech 2025: Detecting AI-generated Product Reviews in Dravidian Languages. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 187–190, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.