ymoslem/acl-abstracts huggingface dataset:
Misogynistic Meme Detection in Dravidian Languages Using Kolmogorov Arnold-based Networks (2025)

The prevalence of misogynistic content online poses significant challenges to ensuring a safe and inclusive digital space for women. This study presents a pipeline to classify online memes as misogynistic or non misogynistic. The pipeline combines contextual image embeddings generated using the Vision Transformer Encoder (ViTE) model with text embeddings extracted from the memes using ModernBERT. These multimodal embeddings were fused and trained using three advanced types of Kolmogorov Artificial Networks (KAN): PyKAN, FastKAN, and Chebyshev KAN. The models were evaluated based on their F1 scores, demonstrating their effectiveness in addressing this issue. This research marks an important step towards reducing offensive online content, promoting safer and more respectful interactions in the digital world.
HTMS@DravidianLangTech 2025: Fusing TF-IDF and BERT with Dimensionality Reduction for Abusive Language Detection in Tamil and Malayalam (2025)

Detecting abusive and similarly toxic content posted on a social media platform is challenging due to the complexities of the language, data imbalance, and the code-mixed nature of the text. In this paper, we present our submissions for the shared task on abusive Tamil and Malayalam texts targeting women on social media{---}DravidianLangTech@NAACL 2025. We propose a hybrid embedding model that integrates embeddings generated using term frequency-inverse document frequency (TF-IDF) and BERT. To get rid of the differences in the embedding dimensions, we used a dimensionality reduction method with TF-IDF embedding. We submitted two more runs to the shared task, which involve a model based on TF-IDF embedding and another based on BERT-based embedding. The code for the submissions is available at https://github.com/Tarrruh/NLP{\_}HTMS.
Team\_Catalysts@DravidianLangTech 2025: Leveraging Political Sentiment Analysis using Machine Learning Techniques for Classifying Tamil Tweets (2025)

This work proposed a methodology for assessing political sentiments in Tamil tweets using machine learning models. The approach addressed linguistic challenges in Tamil text, including cleaning, normalization, tokenization, and class imbalance, through a robust preprocessing pipeline. Various models, including Random Forest, Logistic Regression, and CatBoost, were applied, with Random Forest achieving a macro F1-score of 0.2933 and securing 8th rank among 153 participants in the Codalab competition. This accomplishment highlights the effectiveness of machine learning models in handling the complexities of multilingual, code-mixed, and unstructured data in Tamil political discourse. The study also emphasized the importance of tailored preprocessing techniques to improve model accuracy and performance. It demonstrated the potential of computational linguistics and machine learning in understanding political discourse in low-resource languages like Tamil, contributing to advancements in regional sentiment analysis.
InnovationEngineers@DravidianLangTech 2025: Enhanced CNN Models for Detecting Misogyny in Tamil Memes Using Image and Text Classification (2025)

The rise of misogynistic memes on social media posed challenges to civil discourse. This paper aimed to detect misogyny in Dravidian language memes using a multimodal deep learning approach. We integrated Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), EfficientNet, and a Vision Language Model (VLM) to analyze textual and visual informa tion. EfficientNet extracted image features, LSTM captured sequential text patterns, and BERT learned language-specific embeddings. Among these, VLM achieved the highest accuracy of 85.0{\%} and an F1-score of 70.8, effectively capturing visual-textual relationships. Validated on a curated dataset, our method outperformed baselines in precision, recall, and F1-score. Our approach ranked 12th out of 118 participants for the Tamil language, highlighting its competitive performance. This research emphasizes the importance of multimodal models in detecting harmful content. Future work can explore improved feature fusion techniques to enhance classification accuracy.
MysticCIOL@DravidianLangTech 2025: A Hybrid Framework for Sentiment Analysis in Tamil and Tulu Using Fine-Tuned SBERT Embeddings and Custom MLP Architectures (2025)

Sentiment analysis is a crucial NLP task used to analyze opinions in various domains, including marketing, politics, and social media. While transformer-based models like BERT and SBERT have significantly improved sentiment classification, their effectiveness in low-resource languages remains limited. Tamil and Tulu, despite their widespread use, suffer from data scarcity, dialectal variations, and code-mixing challenges, making sentiment analysis difficult. Existing methods rely on traditional classifiers or word embeddings, which struggle to generalize in these settings. To address this, we propose a hybrid framework that integrates fine-tuned SBERT embeddings with a Multi-Layer Perceptron (MLP) classifier, enhancing contextual representation and classification robustness. Our framework achieves validation F1-scores of 0.4218 for Tamil and 0.3935 for Tulu and test F1-scores of 0.4299 in Tamil and 0.1546 on Tulu, demonstrating its effectiveness. This research provides a scalable solution for sentiment classification in low-resource languages, with future improvements planned through data augmentation and transfer learning. Our full experimental codebase is publicly available at: github.com/ciol-researchlab/NAACL25-Mystic-Tamil-Sentiment-Analysis.
KEC\_AI\_DATA\_DRIFTERS@DravidianLangTech 2025: Fake News Detection in Dravidian Languages (2025)

Detecting fake news in Malayalam possess significant challenges due to linguistic diversity, code-mixing, and the limited availability of structured datasets. We participated in the Fake News Detection in Dravidian Languages shared task, classifying news and social media posts into binary and multi-class categories. Our experiments used traditional ML models: Support Vector Machine (SVM), Random Forest, Logistic Regression, Naive Bayes and transfer learning models: Multilingual Bert (mBERT) and XLNet. In binary classification, SVM achieved the highest macro-F1 score of 0.97, while in multi-class classification, it also outperformed other models with a macro-F1 score of 0.98. Random Forest ranked second in both tasks. Despite their advanced capabilities, mBERT and XLNet exhibited lower precision due to data limitations. Our approach enhances fake news detection and NLP solutions for low-resource languages.
KECEmpower@DravidianLangTech 2025: Abusive Tamil and Malayalam Text targeting Women on Social Media (2025)

The detection of abusive text targeting women, especially in Dravidian languages like Tamil and Malayalam, presents a unique challenge due to linguistic complexities and code-mixing on social media. This paper evaluates machine learning models such as Support Vector Machines (SVM), Logistic Regression (LR), and Random Forest Classifiers (RFC) for identifying abusive content. Code-mixed datasets sourced from platforms like YouTube are used to train and test the models. Performance is evaluated using accuracy, precision, recall, and F1-score metrics. Our findings show that SVM outperforms the other classifiers in accuracy and recall. However, challenges persist in detecting implicit abuse and addressing informal, culturally nuanced language. Future work will explore transformer-based models like BERT for better context understanding, along with data augmentation techniques to enhance model performance. Additionally, efforts will focus on expanding labeled datasets to improve abuse detection in these low-resource languages.
KEC\_AI\_GRYFFINDOR@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian languages (2025)

It is difficult to detect hate speech in codemixed Dravidian languages because the data is multilingual and unstructured. We took part in the shared task to detect hate speech in text and audio data for Tamil, Malayalam, and Telugu in this research. We tested different machine learning and deep learning models such as Logistic Regression, Ridge Classifier, Random Forest, and CNN. For Tamil, Logistic Regression gave the best macro-F1 score of 0.97 for text, whereas Ridge Classifier was the best for audio with a score of 0.75. For Malayalam, Random Forest gave the best F1-score of 0.97 for text, and CNN was the best for audio (F1 score: 0.69). For Telugu, Ridge Classifier gave the best F1-score of 0.89 for text, whereas CNN was the best for audio (F1-score: 0.87).Our findings prove that a multimodal solution effi ciently tackles the intricacy of hate speech detection in Dravidian languages. In this shared task,out of 145 teams we attained the 12th rank for Tamil and 7th rank for Malayalam and Telugu.
KECLinguAIsts@DravidianLangTech 2025: Detecting AI-generated Product Reviews in Dravidian Languages (2025)

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.
Dll5143@DravidianLangTech 2025: Majority Voting-Based Framework for Misogyny Meme Detection in Tamil and Malayalam (2025)

Misogyny memes pose a significant challenge on social networks, particularly in Dravidian-scripted languages, where subtle expressions can propagate harmful narratives against women. This paper presents our approach for the {\textquotedblleft}Shared Task on MisogynyMeme Detection,{\textquotedblright} organized as part of DravidianLangTech@NAACL 2025, focusing on misogyny meme detection in Tamil andMalayalam. To tackle this problem, we proposed a multi-model framework that integrates three distinct models: M1 (ResNet-50 + google/muril-large-cased), M2 (openai/clipvit- base-patch32 + ai4bharat/indic-bert), and M3 (ResNet-50 + ai4bharat/indic-bert). Thefinal classification is determined using a majority voting mechanism, ensuring robustness by leveraging the complementary strengths ofthese models. This approach enhances classification performance by reducing biases and improving generalization. Our model achievedan F1 score of 0.77 for Tamil, significantly improving misogyny detection in the language. For Malayalam, the framework achieved anF1 score of 0.84, demonstrating strong performance. Overall, our method ranked 5th in Tamil and 4th in Malayalam, highlighting itscompetitive effectiveness in misogyny meme detection.
KEC\_AI\_VSS\_run2@DravidianLangTech 2025: Abusive Tamil and Malayalam Text targeting Women on Social Media (2025)

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.
The\_Deathly\_Hallows@DravidianLangTech 2025: AI Content Detection in Dravidian Languages (2025)

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.
SSN\_MMHS@DravidianLangTech 2025: A Dual Transformer Approach for Multimodal Hate Speech Detection in Dravidian Languages (2025)

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

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

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{\%}.
BlueRay@DravidianLangTech-2025: Fake News Detection in Dravidian Languages (2025)

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.
KEC\_AI\_ZEROWATTS@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian languages (2025)

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
MNLP@DravidianLangTech 2025: A Deep Multimodal Neural Network for Hate Speech Detection in Dravidian Languages (2025)

Social media hate speech is a significant issue because it may incite violence, discrimination, and social unrest. Anonymity and reach of such platforms enable the rapid spread of harmful content, targeting individuals or communities based on race, gender, religion, or other attributes. The detection of hate speech is very important for the creation of safe online environments, protection of marginalized groups, and compliance with legal and ethical standards. This paper aims to analyze complex social media content using a combination of textual and audio features. The experimental results establish the effectiveness of the proposed approach, with F1-scores reaching 72{\%} for Tamil, 77{\%} for Malayalam, and 36{\%} for Telugu. Such results strongly indicate that multimodal methodologies have significant room for improvement in hate speech detection in resource-constrained languages and underscore the need to continue further research into this critical area.
MSM\_CUET@DravidianLangTech 2025: XLM-BERT and MuRIL Based Transformer Models for Detection of Abusive Tamil and Malayalam Text Targeting Women on Social Media (2025)

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.
MNLP@DravidianLangTech 2025: Transformer-based Multimodal Framework for Misogyny Meme Detection (2025)

A meme is essentially an artefact of content- usually an amalgamation of a picture, text, or video-content that spreads like wildfire on the internet, usually shared for amusement, cultural expression, or commentary. They are very much similar to an inside joke or a cultural snapshot that reflects shared ideas, emotions, or social commentary, remodulated and reformed by communities. Some of them carry harmful content, such as misogyny. A misogynistic meme is social commentary that espouses negative stereotypes, prejudice, or hatred against women. The detection and addressing of such content will help make the online space inclusive and respectful. The work focuses on developing a multimodal approach for categorizing misogynistic and non-misogynistic memes through the use of pretrained XLM-RoBERTa to draw text features and Vision Transformer to draw image features. The combination of both text and images features are processed into a machine learning and deep learning model which have attained F1-scores 0.77 and 0.88, respectively Tamil and Malayalam for misogynist Meme Dataset.
Code\_Conquerors@DravidianLangTech 2025: Deep Learning Approach for Sentiment Analysis in Tamil and Tulu (2025)

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)