United States

The increasing instances of abusive language against women on social media platforms have brought to the fore the need for effective content moderation systems, especially in low-resource languages like Tamil and Malayalam. This paper addresses the challenge of detecting gender-based abuse in YouTube comments using annotated datasets in these languages. Comments are classified into abusive and non-abusive categories. We applied the following machine learning algorithms, namely Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting and AdaBoost for classification. Micro F1 score of 0.95 was achieved by SVM for Tamil and 0.72 by Random Forest for Malayalam. Our system participated in the shared task on abusive comment detection, out of 160 teams achieving the rank of 13th for Malayalam and rank 34 for Tamil, and both indicate both the challenges and potential of our approach in low-resource language processing. Our findings have highlighted the significance of tailored approaches to language-specific abuse detection.

NAACL 2025

KEC_AI_VSS_run2@DravidianLangTech 2025: Abusive Tamil and Malayalam Text targeting Women on Social Media

abusive comments

content moderation

machine learning classification

low-resource languages

social media

### 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. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

workshop paper

The DravidianLangTech@NAACL 2025 shared task focused on Detecting AI-generated Product Reviews in Dravidian Languages, aiming to address the challenge of distinguishing AI-generated content from human-written reviews in Tamil and Malayalam. As AI generated text becomes more prevalent, ensuring the authenticity of online product reviews is crucial for maintaining consumer trust and preventing misinformation. In this study, we explore various feature extraction techniques, including TF-IDF, Count Vectorizer, and transformer-based embeddings such as BERT-Base-Multilingual-Cased and XLM-RoBERTa-Large, to build a robust classification model. Our approach achieved F1-scores of 0.9298 for Tamil and 0.8797 for Malayalam, ranking 8th in Tamil and 11th in Malayalam among all participants. The results highlight the effectiveness of transformer-based embeddings in differentiating AI-generated and human-written content. This research contributes to the growing body of work on AI-generated content detection, particularly in underrepresented Dravidian languages, and provides insights into the challenges unique to these languages.

The_Deathly_Hallows@DravidianLangTech 2025: AI Content Detection in Dravidian Languages

The proliferation of the Internet and social media platforms has resulted in an alarming increase in online hate speech, negatively affecting individuals and communities worldwide. While most research focuses on text-based detection in English, there is an increasing demand for multilingual and multimodal approaches to address hate speech more effectively. This paper presents a methodology for multiclass hate speech classification in low-resource Indian languages namely, Malayalam, Telugu, and Tamil, as part of the shared task at DravidianLangTech 2025. Our proposed approach employs a dual transformer-based framework that integrates audio and text modalities, facilitating cross-modal learning to enhance detection capabilities. Our model achieved macro-F1 scores of 0.348, 0.1631, and 0.1271 in the Malayalam, Telugu, and Tamil subtasks respectively. Although the framework's performance is modest, it provides valuable insights into the complexities of multimodal hate speech detection in low-resource settings and highlights areas for future improvement, including data augmentation, and alternate fusion and feature extraction techniques.

SSN_MMHS@DravidianLangTech 2025: A Dual Transformer Approach for Multimodal Hate Speech Detection in Dravidian Languages

This paper presents our approach to the Shared Task on Detecting AI-Generated Product Reviews in Dravidian Languages as part of DravidianLangTech@NAACL 2025. The task focuses on distinguishing between human-written and AI-generated reviews in Tamil and Malayalam, languages rich in linguistic complexities. Using the provided datasets, we implemented machine learning and deep learning models, including Logistic Regression (LR), Support Vector Machine (SVM), and BERT. Through preprocessing techniques like tokenization and TF-IDF vectorization, we achieved competitive results, with our SVM and BERT models demonstrating superior performance in Tamil and Malayalam respectively. Our findings underscore the unique challenges of working with Dravidian languages in this domain and highlight the importance of robust feature extraction.

InnovateX@DravidianLangTech 2025: Detecting AI-Generated Product Reviews in Dravidian Languages

The introduction of Jio in India has significantly increased the number of social media users, particularly on platforms like X (Twitter), Facebook, Instagram. While this growth is positive, it has also led to a rise in native language speakers, making social media analysis more complex. In this study, we focus on Tamil, a Dravidian language, and aim to classify social media comments from X (Twitter) into seven different categories. Tamil speaking users often communicate using a mix of Tamil and English, creating unique challenges for analysis and tracking. This surge in diverse language usage on social media highlights the need for robust sentiment analysis tools to ensure the platform remains accessible and user-friendly for everyone with different political opinions. In this study we trained four machine learning models, SGD Classifier, Random Forest Classifier, Decision Tree, and Multinomial Naive Bayes classifier to identify and classify the comments. Among these, the SGD Classifier achieved the best performance, with a training accuracy of 83.67% and a validation accuracy of 80.43%.

KSK@DravidianLangTech 2025: Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments Using Incremental Learning

The rise of fake news presents significant issues, particularly for underrepresented lan guages. This study tackles fake news identification in Dravidian languages with two subtasks: binary classification of YouTube comments and multi-class classification of Malayalam news into five groups. Text preprocessing, vectorization, and transformer-based embeddings are all part of the methodology, including baseline comparisons utilizing classic machine learning, deep learning, and transfer learning models. In Task 1, our solution placed 17th, displaying acceptable binary classification per formance. In Task 2, we finished eighth place by effectively identifying nuanced categories of Malayalam news, demonstrating the efficacy of transformer-based models.

BlueRay@DravidianLangTech-2025: Fake News Detection in Dravidian Languages

Hate speech detection in code-mixed Dravidian languages presents significant challenges due to the multilingual and unstructured nature of the data. In this work, we participated in the shared task to detect hate speech in Tamil, Malayalam, and Telugu using both text and audio data. We explored various machine learning models, including Logistic Regression, Ridge Classifier, Random Forest, and Convolutional Neural Networks (CNN). For Tamil text data, Logistic Regression achieved the highest macro-F1 score of 0.97, while Ridge Classifier performed best for audio with 0.75. In Malayalam, Random Forest excelled for text with 0.97, and CNN for audio with 0.69. For Telugu, Ridge Classifier achieved 0.89 for text and CNN 0.87 for audio.These results demonstrate the efficacy of our multimodal approach in addressing the complexity of hate speech detection across the Dravidian languages.Tamil:11th rank, Malayalam :6th rank,Telugu:8th rank among 145 teams

KEC_AI_ZEROWATTS@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian languages

Social media has evolved into an excellent platform for presenting ideas, viewpoints, and experiences in modern society. But this large domain has also brought some alarming problems including internet misuse. Targeted specifically at certain groups like women, abusive language is pervasive on social media. The task is always difficult to detect abusive text for low-resource languages like Tamil, Malayalam, and other Dravidian languages. It is crucial to address this issue seriously, especially for Dravidian languages. This paper presents a novel approach to detecting abusive Tamil and Malayalam texts targeting social media. A shared task on Abusive Tamil and Malayalam Text Targeting Women on Social Media Detection has been organized by DravidianLangTech at NAACL-2025. The organizer has provided an annotated dataset that labels two classes: Abusive and Non-Abusive. We have implemented our model with different transformer-based models like XLM-R, MuRIL, IndicBERT, and mBERT transformers and the Ensemble method with SVM and Random Forest for training. We selected XLM-RoBERT for Tamil text and MuRIL for Malayalam text due to their superior performance compared to other models. After developing our model, we tested and evaluated it on the DravidianLangTech@NAACL 2025 shared task dataset. We found that XLM-R has provided the best result for abusive Tamil text detections with an F1 score of 0.7873 on the test set and ranked 2nd position among all participants. On the other hand, MuRIL has provided the best result for abusive Malayalam text detections with an F1 score of 0.6812 and ranked 10th among all participants.

MSM_CUET@DravidianLangTech 2025: XLM-BERT and MuRIL Based Transformer Models for Detection of Abusive Tamil and Malayalam Text Targeting Women on Social Media

In this paper we propose a novel approach to sentiment analysis in languages with mixed Dravidian codes, specifically Tamil-English and Tulu-English social media text. We introduce an innovative hybrid deep learning architecture that uniquely combines convolutional and recurrent neural networks to effectively capture both local patterns and long-term dependencies in code-mixed text. Our model addresses critical challenges in low-resource language processing through a comprehensive preprocessing pipeline and specialized handling of class imbalance and out-of-vocabulary words. Evaluated on a substantial dataset of social media comments, our approach achieved competitive macro F1 scores of 0.3357 for Tamil (ranked 18) and 0.3628 for Tulu (ranked 13)

Code_Conquerors@DravidianLangTech 2025: Deep Learning Approach for Sentiment Analysis in Tamil and Tulu

The growing prevalence of code-mixed text on social media presents unique challenges for sen- timent analysis, particularly in low-resource languages like Tamil and Tulu. This paper ex- plores sentiment classification in Tamil-English and Tulu-English code-mixed datasets using both machine learning (ML) and deep learn- ing (DL) approaches. The ML model utilizes TF-IDF feature extraction combined with a Logistic Regression classifier, while the DL model employs FastText embeddings and a BiLSTM network enhanced with an attention mechanism. Experimental results reveal that the ML model outperforms the DL model in terms of macro F1-score for both languages. Specifically, for Tamil, the ML model achieves a macro F1-score of 0.46, surpassing the DL model’s score of 0.43. For Tulu, the ML model significantly outperforms the DL model, achiev- ing 0.60 compared to 0.48. This performance disparity is more pronounced in Tulu due to its smaller dataset size of 13,308 samples com- pared to Tamil’s 31,122 samples, highlight- ing the data efficiency of ML models in low- resource settings. The study provides insights into the strengths and limitations of each ap- proach, demonstrating that traditional ML tech- niques remain competitive for code-mixed sen- timent analysis when data is limited. These findings contribute to ongoing research in mul- tilingual NLP and offer practical implications for applications such as social media monitor- ing, customer feedback analysis, and conversa- tional AI in Dravidian languages.

JustATalentedTeam@DravidianLangTech 2025: A Study of ML and DL approaches for Sentiment Analysis in Code-Mixed Tamil and Tulu Texts

This research focuses on misogyny detection in Dravidian languages using multimodal techniques. It leverages advanced machine learning models, including Vision Transformers (ViT) for image analysis and BERT-based transformers for text processing. The study highlights the challenges of working with regional datasets and addresses these with innovative preprocessing and model training strategies. The evaluation reveals significant improvements in detection accuracy, showcasing the potential of multimodal approaches in combating online abuse in underrepresented languages.

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