United States

Detecting AI-generated product reviews is a critical challenge in natural language processing (NLP), particularly for low-resource languages like Malayalam. In this study, we propose a large language model (LLM)-based approach to identify AI-generated reviews in Dravidian languages, focusing on the product review domain. We systematically evaluated multiple LLMs on a dedicated Malayalam dataset to assess their effectiveness in distinguishing between human-written and AI-generated reviews. Our experiments demonstrate that the Gemma-2B model outperforms other models, achieving a macro F1-score of 89.99%. Our approach secured 5^{th} place in the DravidianLangTech@NAACL 2025 shared task for Malayalam, highlighting the potential of LLMs in tackling the challenges of AI-generated review detection in low-resource languages. Our findings highlight the potential of LLMs in detecting AI-generated content in underrepresented languages, contributing to advancements in Dravidian language processing and the broader field of AI-generated content identification.

NAACL 2025

CUET_NLP_FiniteInfinity@DravidianLangTech 2025: Exploring Large Language Models for AI-Generated Product Review Classification in Malayalam

ai generated text

malayalam

low resourced languages

### 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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workshop paper

The shared task on Detecting AI-generated Product Reviews in Dravidian Languages is aimed at addressing the growing concern of AI-generated product reviews, specifically in Malayalam and Tamil. As AI tools become more advanced, the ability to distinguish between human-written and AI-generated content has become increasingly crucial, especially in the domain of online reviews where authenticity is essential for consumer decision-making. In our approach, we used the ALBERT, IndicBERT, and Support Vector Machine (SVM) models to classify the reviews. The results of our experiments demonstrate the effectiveness of our methods in detecting AI-generated content.

AnalysisArchitects@DravidianLangTech 2025: BERT Based Approach For Detecting AI Generated Product Reviews In Dravidian Languages

Sentiment analysis is recognized as an important area in Natural Language Processing (NLP) that aims at understanding and classifying opinions or emotions in text. In the political field, public sentiment is analyzed to gain insight into opinions, address issues, and shape better policies. Social media platforms like Twitter (now X) are widely used to express thoughts and have become a valuable source of real-time political discussions. In this paper, the shared task of Political Multiclass Sentiment Analysis of Tamil tweets is examined, where the objective is to classify tweets into specific sentiment categories. The proposed approach is explained, which involves preprocessing Tamil text, extracting useful features, and applying machine learning and deep learning models for classification. The effectiveness of the methods is demonstrated through experimental results and the challenges encountered while working on the analysis of Tamil political sentiment are discussed.

AnalysisArchitects@DravidianLangTech 2025: Machine Learning Approach to Political Multiclass Sentiment Analysis of Tamil

This study focuses on detecting misogynistic content in memes under the title Misogynistic. Meme Detection Using Multimodal Deep Learning. Through an analysis of both textual and visual components of memes, specifically in Tamil, the study seeks to detect misogynistic rhetoric directed towards women. Preprocessing and vectorizing text data using methods like TF-IDF, GloVe, Word2Vec, and transformer-based embeddings like BERT are all part of the textual analysis process. Deep learning models like ResNet and EfficientNet are used to extract significant image attributes for the visual component. To improve classification performance, these characteristics are then combined in a multimodal framework employing hybrid architectures such as CNN-LSTM, GRU-EfficientNet, and ResNet-BERT. The classification of memes as misogynistic or non-misogynistic is done using sophisticated machine learning and deep learning ap proaches. Model performance is evaluated using metrics like Accuracy, Precision, Recall, F1-Score, and Macro Average F1-Score. This study shows how multimodal deep learning can effectively detect and counteract negative narratives about women in digital media by combining natural language processing with image classification.

TEAM_STRIKERS@DravidianLangTech2025: Misogyny Meme Detection in Tamil Using Multimodal Deep Learning

The rapid spread of misinformation on social media platforms necessitates robust detection mechanisms, particularly for languages with limited computational resources. This paper presents our system for the DravidianLangTech 2025 shared task on Fake News Detection in Malayalam YouTube comments, addressing both binary and multiclass classification challenges. We propose a Multi-Pooling Feature Fusion (MPFF) architecture that leverages [CLS] + Mean + Max pooling strategy with transformer models. Our system demonstrates strong performance across both tasks, achieving a macro-averaged F1 score of 0.874, ranking 6th in binary classification, and 0.628, securing 1st position in multiclass classification. Experimental results show that our MPFF approach with XLM-RoBERTa significantly outperforms traditional machine learning and deep learning baselines, particularly excelling in the more challenging multiclass scenario. These findings highlight the effectiveness of our methodology in capturing nuanced linguistic features for fake news detection in Malayalam, contributing to the advancement of automated verification systems for Dravidian languages.

KCRL@DravidianLangTech 2025: Multi-Pooling Feature Fusion with XLM-RoBERTa for Malayalam Fake News Detection and Classification

The proliferation of social media platforms has resulted in increased instances of online abuse, particularly targeting marginalized groups such as women. This study focuses on the classification of abusive comments in Tamil and Malayalam, two Dravidian languages widely spoken in South India. Leveraging a multilingual BERT model, this paper provides an effective approach for detecting and categorizing abusive and non-abusive text. Using labeled datasets comprising social media comments, our model demonstrates its ability to identify targeted abuse with promising accuracy. This paper outlines the dataset preparation, model architecture, training methodology, and the evaluation of results, providing a foundation for combating online abuse in low-resource languages.The methodology is unique for its integration of multilingual BERT and weighted loss functions to address class imbalance, showcasing a pathway for effective abuse detection in other underrepresented languages. The BERT model achieved an F1-score of 0.6519 for Tamil and 0.6601 for Malayalam. The code
for this work is available on github Abusive-Text-targeting-women

SSN_IT_NLP@DravidianLangTech 2025: Abusive Tamil and Malayalam Text targeting Women on Social Media

Recent advancements in natural language processing (NLP) have enabled artificial intelligence (AI) models to generate product reviews
that are indistinguishable from those written by humans. To address these concerns, this study proposes an effective AI detector model capable of differentiating between AI-generated and human-written product reviews. Our methodology incorporates various machine learning techniques, including Naive Bayes, Random Forest, Logistic Regression, SVM, and deep learning approaches based on the BERT architecture.Our findings reveal that BERT outperforms other models in detecting AI-generated content in both Tamil product reviews and Malayalam product reviews.

TeamVision@DravidianLangTech 2025: Detecting AI generated product reviews in Dravidian Languages

The rise in utilizing social media platforms increased user-generated content significantly, including negative comments about women in Tamil and Malayalam. While these platforms encourage communication and engagement, they also become a medium for the spread of abusive language, which poses challenges to maintaining a safe online environment for women. Prevention of usage of abusive content against women as much as possible is the main issue focused in the research. This research focuses on detecting abusive language against women in Tamil and Malayalam social media comments using computational models, such as Logistic regression model, Support vector machines (SVM) model, Random forest model, multilingual BERT model, XLM-Roberta model, and IndicBERT. These models were trained and tested on a specifically curated dataset containing labeled comments in both languages. Among all the approaches, IndicBERT achieved a highest macro F1-score of 0.75. The findings emphasize the significance of employing a combination of traditional and advanced computational techniques to address challenges in Abusive Content Detection (ACD) specific to regional languages.

CoreFour_IIITK@DravidianLangTech 2025: Abusive Content Detection Against Women Using Machine Learning And Deep Learning Models

The DravidianLangTech@NAACL 2025 shared task focused on multimodal hate speech detection in Tamil, Telugu, and Malayalam using social media text and audio. Our approach integrated advanced preprocessing, feature extraction, and deep learning models. For text, preprocessing steps included normalization, tokenization, stopword removal, and data augmentation. Feature extraction was performed using TF-IDF, Count Vectorizer, BERT-base-multilingual-cased, XLM-Roberta-Base, and XLM-Roberta-Large, with the latter achieving the best performance. The models attained training accuracies of 83% (Tamil), 88% (Telugu), and 85% (Malayalam). For audio, Mel Frequency Cepstral Coefficients (MFCCs) were extracted and enhanced with augmentation techniques such as noise addition, time-stretching, and pitch-shifting. A CNN-based model achieved training accuracies of 88% (Tamil), 88% (Telugu), and 93% (Malayalam). Macro F1 scores ranked Tamil 3rd (0.6438), Telugu 15th (0.1559), and Malayalam 12th (0.3016). Our study highlights the effectiveness of text-audio fusion in hate speech detection and underscores the importance of preprocessing, multimodal techniques, and feature augmentation in addressing hate speech on social media.

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

Fake news detection in Malayalam is difficult due to limited data and language challenges. This study compares machine learning, deep learning, and transformer models for classification. The dataset is balanced and divided into training, development and test sets. Machine learning models (SVM, Random Forest, Naive Bayes) used TF-IDF features and deep learning models (LSTM, BiLSTM, CNN) worked with tokenized sequences. We fine-tuned transformer models like IndicBERT, MuRIL, mBERT, and Malayalam-Bert. Among them, the Malayalam-Bert model performed the best and achieved an F1 score of 86%. On the other hand mBERT performed best at spotting fake news. However, the models struggled with mixed-language text and complex writing. Despite these challenges, transformer models turned out to be the most effective for detecting fake news in Malayalam.

Celestia@DravidianLangTech 2025: Malayalam-BERT and m-BERT based transformer models for Fake News Detection in Dravidian Languages

This paper presents an in-depth study on multi modal hate speech detection in Dravidian lan guages—Tamil, Telugu, and Malayalam—by leveraging both audio and text modalities. De tecting hate speech in these languages is partic ularly challenging due to factors such as code mixing, limited linguistic resources, and di verse cultural contexts. Our approach integrates advanced techniques for audio feature extrac tion and XLM-Roberta for text representation, with feature alignment and fusion to develop a robust multimodal framework. The dataset is carefully categorized into labeled classes: gender-based, political, religious, and personal defamation hate speech, along with a non-hate category. Experimental results indicate that our model achieves a macro F1-score of 0.76 and an accuracy of approximately 85.

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AnalysisArchitects@DravidianLangTech 2025: BERT Based Approach For Detecting AI Generated Product Reviews In Dravidian Languages

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Overview of the Shared Task on Detecting AI generated Product Reviews in Dravidian Languages: DravidianLangTech@NAACL 2025