United States

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.

NAACL 2025

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

feature extraction

fake news

machine learning

deep learning

### 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

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.

Code_Conquerors@DravidianLangTech 2025: Multimodal Misogyny Detection in Dravidian Languages Using Vision Transformer and BERT

The sentiment analysis in code-mixed Dravidian languages such as Tamil-English and Tulu-English is the focus of this study because these languages present difficulties for conventional techniques. In this work, We used ensembles, multilingual Bidirectional Encoder Representation (mBERT), Bidirectional Long Short Term Memory (BiLSTM), Random Forest (RF), Support Vector Machine (SVM), and preprocessing in conjunction with Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec feature extraction. mBERT obtained accuracy of 64% for Tamil and 68% for Tulu on development datasets. In test sets, the ensemble model gave Tamil a macro F1-score of 0.4117, while mBERT gave Tulu a macro F1-score of 0.5511. With regularization and data augmentation, these results demonstrate the approach’s potential for further advancements.

YenLP_CS@DravidianLangTech 2025: Sentiment Analysis on Code-Mixed Tamil-Tulu Data Using Machine Learning and Deep Learning Models

Sentiment analysis in code-mixed languages, particularly Tamil-English, is a growing challenge in natural language processing (NLP) due to the prevalence of multilingual communities on social media. This paper explores various machine learning and transformer-based models, including Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), BERT, and mBERT, for sentiment classification of Tamil-English code-mixed text. The models are evaluated on a shared task dataset provided by DravidianLangTech@NAACL 2025, with performance measured through accuracy, precision, recall, and F1-score. Our results demonstrate that transformer-based models, particularly mBERT, outperform traditional classifiers in identifying sentiment polarity. Future work aims to address the challenges posed by code-switching and class imbalance through advanced model architectures and data augmentation techniques.

KEC-Elite-Analysts@DravidianLangTech 2025: Deciphering Emotions in Tamil-English and Code-Mixed Social Media Tweets

As social media has grown, so has online abuse, with women often facing harmful online behavior. This discourages their free participation and expression online. This paper outlines the approach adopted by our team for detecting abusive comments in Tamil and Malayalam. The task focuses on classifying whether a given comment contains abusive language towards women. We experimented with transformer based models by fine-tuning Tamil-BERT for Tamil and Malayalam-BERT for Malayalam. Additionally, we fine-tuned IndicBERT v2 on both Tamil and Malayalam datasets. To evaluate the effect of pre-processing, we also conducted experiments using non-preprocessed text. Results demonstrate that IndicBERT v2 outperformed the language-specific BERT models in both languages. Pre-processing the data showed mixed results, with a slight improvement in the Tamil dataset but no significant benefit for the Malayalam dataset. Our approach secured first place in Tamil with a macro F1-score of 0.7883 and second place in Malayalam with a macro F1-score of 0.7234. The implementation details of the task will be found in the GitHub repository.

CUET_Agile@DravidianLangTech 2025: Fine-tuning Transformers for Detecting Abusive Text Targeting Women from Tamil and Malayalam Texts

Misogynous content on social media, especially in memes, present challenges due to the complex reciprocation of text and images that carry offensive messages. This difficulty mostly arises from the lack of direct alignment between modalities and biases in large-scale visio-linguistic models. In this paper, we present our system for the Shared Task on Misogyny Meme Detection - DravidianLangTech@NAACL 2025. We have implemented various unimodal models, such as mBERT and IndicBERT for text data, and ViT, ResNet, and EfficientNet for image data. Moreover, we have tried combining these models and finally adopted a multimodal approach that combined mBERT for text and EfficientNet for image features, both fine-tuned to better interpret subtle language and detailed visuals. The fused features are processed through a dense neural network for classification. Our approach achieved an F1 score of 0.78120, securing 4th place and demonstrating the potential of transformer-based architectures and state-of-the-art CNNs for this task.

CUET-823@DravidianLangTech 2025: Shared Task on Multimodal Misogyny Meme Detection in Tamil Language

Sentiment analysis, the task of identifying subjective opinions or emotional responses, has become increasingly significant with the rise of social media. However, analysing sentiment in Dravidian languages such as Tamil-English and Tulu-English presents unique challenges due to linguistic code-switching (where people tend to mix multiple languages) and non-native scripts. Traditional monolingual sentiment analysis models struggle to address these complexities effectively. This research explores a fine-tuned transformer model based on the XLM-RoBERTa model for sentiment detection. It utilizes the tokenizer from the XLM-RoBERTa model for text preprocessing. Additionally, the performance of the XLM-RoBERTa model was compared with traditional machine learning models such as Logistic Regression (LR) and Random Forest (RF), as well as other transformer-based models like BERT and RoBERTa. This research was based on our work for the Sentiment Analysis in Tamil and Tulu DravidianLangTech@NAACL 2025 competition, where we received a macro F1-score of 59% for the Tulu dataset and 49% for the Tamil dataset, placing third in the competition.

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