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The increasing prevalence of AI-generated content has raised concerns about the authenticity and reliability of online reviews, particularly in resource-limited languages like Tamil and Malayalam. This paper presents an approach to the Shared Task on Detecting AI-generated Product Reviews in Dravidian Languages at NAACL2025, which focuses on distinguishing AI-generated reviews from human-written ones in Tamil and Malayalam. Several transformer-based models, including IndicBERT, RoBERTa, mBERT, and XLM-R, were evaluated, with language-specific BERT models for Tamil and Malayalam demonstrating the best performance. The chosen methodologies were evaluated using Macro Average F1 score. In the rank list released by the organizers, team SSNTrio, achieved ranks of 3rd and 29th for the Malayalam and Tamil datasets with Macro Average F1 Scores of 0.914 and 0.598 respectively.

NAACL 2025

SSNTrio@DravidianLangTech 2025: Identification of AI Generated Content in Dravidian Languages using Transformers

ai generated content

dravidian languages

bert models

augmentation

transformers

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

workshop paper

This paper presents an approach to sentiment analysis for code-mixed Tamil-English and Tulu-English datasets as part of the DravidianLangTech@NAACL 2025 shared task. Sentiment analysis, the process of determining the emotional tone or subjective opinion in text, has become a critical tool in analyzing public sentiment on social media platforms. The approach discussed here uses multilingual BERT (mBERT) fine-tuned on the provided datasets to classify sentiment polarity into various predefined categories: for Tulu, the categories were positive, negative, not_tulu, mixed, and neutral; for Tamil, the categories were positive, negative, unknown, mixed_feelings, and neutral. The mBERT model demonstrates its effectiveness in handling sentiment analysis for codemixed and resource-constrained languages by achieving an F1-score of 0.44 for Tamil, securing the 6th position in the ranklist; and 0.56 for Tulu, ranking 5th in the respective task.

SSNTrio@DravidianLangTech 2025: Sentiment Analysis in Dravidian Languages using Multilingual BERT

The advent and expansion of social media have transformed global communication. Despite its numerous advantages, it has also created an avenue for the rapid spread of fake news, which can impact people's decision-making and judgment. This study explores detecting fake news as part of the DravidianLangTech@NAACL 2025 shared task, focusing on two key tasks. The aim of Task 1 is to classify Malayalam social media posts as either original or fake, and Task 2 categorizes Malayalam-language news articles into five levels of truthfulness: False, Half True, Mostly False, Partly False, and Mostly True. We accomplished the tasks using transformer models, e.g., M-BERT and classifiers like Naive Bayes. Our results were promising, with M-BERT achieving the better results. We achieved a macro-F1 score of 0.83 for distinguishing between fake and original content in Task 1 and a score of 0.54 for classifying news articles in Task 2, ranking us 11 and 4, respectively.

NLP_goats@DravidianLangTech 2025: Detecting Fake News in Dravidian Languages: A Text Classification Approach

Social media in the present world is an essential communication platform for information sharing. But their emergence has now led to an increase in the proportion of online abuse, in particular against women in the form of abusive and offensive messages. A reflection of the social inequalities, the importance of detecting abusive language is highlighted by the fact that the usage has a profound psychological and social impact on the victims. This work by DravidianLangTech@NAACL 2025 aims at developing an automated abusive content detection system for women directed towards women on the Tamil and Malayalam platforms, two of the Dravidian languages. Based on a dataset of their YouTube comments about sensitive issues, the study uses multilingual BERT (mBERT) to detect abusive comments versus non-abusive ones. We achieved F1 scores of 0.75 in Tamil and 0.68 in Malayalam, placing us 13 and 9 respectively.

NLP_goats@DravidianLangTech 2025: Towards Safer Social Media: Detecting Abusive Language Directed at Women in Dravidian Languages

Misogynistic memes on social media perpetuate gender stereotypes, contribute to harassment, and suppress feminist activism. However, most existing misogyny detection models focus on high-resource languages, leaving a gap in low-resource settings. This work addresses that gap by focusing on misogynistic memes in Tamil and Malayalam, two Dravidian languages with limited resources. We combine computer vision and natural language processing for multi-modal detection, using CLIP embeddings for the vision component and BERT models trained on code-mixed hate speech datasets for the text component. Our results show that this integrated approach effectively captures the unique characteristics of misogynistic memes in these languages, achieving competitive performance with a Macro F1 Score of 0.7800 for the Tamil test set and 0.8748 for the Malayalam test set. These findings highlight the potential of multimodal models and the adaptation of pre-trained models to specific linguistic and cultural contexts, advancing misogyny detection in low-resource settings. Code available at https://github.com/HerWILL-Inc/NAACL-2025

HerWILL@DravidianLangTech 2025: Ensemble Approach for Misogyny Detection in Memes Using Pre-trained Text and Vision Transformers

Fake news detection is a crucial task in combat- ing misinformation, particularly in underrepresented languages such as Malayalam. This paper focuses on detecting fake news in Dravidian languages using two tasks: Social Media Text Classification and News Classification. We employ a fine-tuned multilingual BERT (mBERT) model for classifying a given social media text into original or fake and an LSTM-based architecture for accurately detecting and classifying fake news articles in the Malayalam language into different categories. Extensive preprocessing techniques, such as tokenization and text cleaning, were used to ensure data quality. Our experiments achieved significant accuracy rates and F1- scores. The study’s contributions include applying advanced machine learning techniques to the Malayalam language, addressing the lack of research on low-resource languages, and highlighting the challenges of fake news detection in multilingual and code-mixed environments.

Cognitext@DravidianLangTech2025: Fake News Classification in Malayalam Using mBERT and LSTM

The rise of AI-generated content has introduced challenges in distinguishing machine-generated text from human-written text, particularly in low-resource languages. The identification of artificial intelligence (AI)-based reviews is of significant importance to preserve trust and authenticity on online platforms. The Shared Task on Detecting AI-Generated Product Reviews in Dravidian languages deals with the task of detecting AI-generated and human-written reviews in Tamil and Malayalam. To solve this problem, we specifically fine-tuned mBERT for binary classification. Our system achieved 10th place in Tamil with a macro F1-score of 0.90 and 28th place in Malayalam with a macro F1-score of 0.68, as reported by the NAACL 2025 organizers. The findings demonstrate the complexity involved in the separation of AI-derived text from human-authored writing, with a call for continued advances in detection methods.

NLP_goats_DravidianLangTech_2025__Detecting_AI_Written_Reviews_for_Consumer_Trust

Detecting AI-generated reviews is crucial for maintaining the authenticity of online feedback in low-resource languages like Tamil and Malayalam. We propose a transfer learning-based approach using embeddings from XLM-RoBERTa, IndicBERT, mT5, and Sentence-BERT, validated with five-fold cross-validation via XGBoost. These embeddings are used to train deep neural networks (DNNs), refined through a weighted ensemble model. Our method achieves 90% F1-score for Malayalam and 73% for Tamil, demonstrating the effectiveness of transfer learning and ensembling for review detection. The source code is publicly available to support further research and improve online review systems in multilingual settings.

RATHAN@DravidianLangTech 2025: Annaparavai - Separate the Authentic Human Reviews from AI-generated one

Social media is a powerful communication tool and rich in diverse content requiring innovative approaches to understand nuances of the lan- guages. Addressing challenges like hate speech necessitates multimodal analysis that integrates textual, and other cues to capture its context and intent effectively. This paper proposes a multimodal hate speech detection system in Tamil, which uses textual and audio features for classification. Our proposed system uses a fine-tuned Indic-BERT model for text based hate speech detection and Wav2Vec2 model for audio based hate speech detection of au- dio data. The fine-tuned Indic-BERT model with Whisper achieved an F1 score of 0.25 on Multimodal approach. Our proposed approach ranked at 10th position in the shared task on Multimodal Hate Speech Detection in Dravid- ian languages at the NAACL 2025 Workshop DravidianLangTech.

DLRG@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages

Ensuring a safe and inclusive online environment requires effective hate speech detection on social media. While detection systems have significantly advanced for English, many regional languages, including Malayalam, Tamil and Telugu, remain underrepresented, creating challenges in identifying harmful content accurately. These languages present unique challenges due to their complex grammar, diverse dialects, and frequent code-mixing with English. The rise of multimodal content, including text and audio, adds further complexity to detection tasks. The shared task ``Multimodal Hate Speech Detection in Dravidian Languages: DravidianLangTech@NAACL 2025'' has aimed to address these challenges. A Youtube-sourced dataset has been provided, labeled into five categories: Gender (G), Political (P), Religious (R), Personal Defamation (C) and Non-Hate (NH). In our approach, we have used mBERT, T5 for text and Wav2Vec2 and Whisper for audio. T5 has performed poorly compared to mBERT, which has achieved the highest F1 scores on the test dataset. For audio, Wav2Vec2 has been chosen over Whisper because it processes raw audio effectively using self-supervised learning. In the hate speech detection task, we have achieved a macro F1 score of 0.2005 for Malayalam, ranking 15th in this task, 0.1356 for Tamil and 0.1465 for Telugu, with both ranking 16th in this task.

Team ML_Forge@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages

Abusive comment detection in low-resource languages is a challenging task particularly when addressing gender-based abuse. Iden tifying abusive language targeting women is crucial for effective content moderation and fostering safer online spaces. A shared task on abusive comment detection in Tamil text organized by DravidianLangTech@NAACL 2025 allowed us to address this challenge using a curated dataset. For this task, we experimented with various machine learning (ML) and deep learning (DL) models includ ing Logistic Regression, Random Forest, SVM, CNN, LSTM, BiLSTMand transformer-based models such as mBERT, IndicBERT, XLM RoBERTa and many more. The dataset com prised of Tamil YouTube comments anno tated with binary labels, Abusive and Non Abusive capturing explicit abuse, implicit bi ases and stereotypes. Our experiments demon strated that XLM-RoBERTa achieved the high est macro F1-score(0.80), highlighting its effec tiveness in handling Tamil text. This research contributes to advancing abusive language de tection and natural language processing in low resource languages particularly for addressing gender-based abuse online.

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