GenAI4ELab/papercli-papers huggingface dataset:
NLP_goats@DravidianLangTech 2025: Detecting Fake News in Dravidian Languages: A Text Classification Approach
Authors: Srihari V K; Vijay Karthick Vaidyanathan; Thenmozhi Durairaj
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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: Towards Safer Social Media: Detecting Abusive Language Directed at Women in Dravidian Languages
Authors: Vijay Karthick Vaidyanathan; Srihari V K; Thenmozhi Durairaj
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
HerWILL@DravidianLangTech 2025: Ensemble Approach for Misogyny Detection in Memes Using Pre-trained Text and Vision Transformers
Authors: Neelima Monjusha Preeti; Trina Chakraborty; Noor Mairukh Khan Arnob; Saiyara Mahmud; Azmine Toushik Wasi
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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
Cognitext@DravidianLangTech2025: Fake News Classification in Malayalam Using mBERT and LSTM
Authors: Shriya Alladi; Bharathi B
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
NLP_goats_DravidianLangTech_2025__Detecting_AI_Written_Reviews_for_Consumer_Trust
Authors: Srihari V K; Vijay Karthick Vaidyanathan; Mugilkrishna D U; Thenmozhi Durairaj
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
RATHAN@DravidianLangTech 2025: Annaparavai - Separate the Authentic Human Reviews from AI-generated one
Authors: Jubeerathan Thevakumar; Luheerathan Thevakumar
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
DLRG@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages
Authors: Ratnavel Rajalakshmi; Ramesh Kannan; Meetesh Saini; Bitan Mallik
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Social media is a powerful communication tooland rich in diverse content requiring innovativeapproaches to understand nuances of the lan-guages. Addressing challenges like hate speechnecessitates multimodal analysis that integratestextual, and other cues to capture its contextand intent effectively. This paper proposes amultimodal hate speech detection system inTamil, which uses textual and audio featuresfor classification. Our proposed system usesa fine-tuned Indic-BERT model for text basedhate speech detection and Wav2Vec2 modelfor audio based hate speech detection of au-dio data. The fine-tuned Indic-BERT modelwith Whisper achieved an F1 score of 0.25 onMultimodal approach. Our proposed approachranked at 10th position in the shared task onMultimodal Hate Speech Detection in Dravid-ian languages at the NAACL 2025 WorkshopDravidianLangTech.
Team ML_Forge@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages
Authors: Adnan Faisal; Shiti Chowdhury; Sajib Bhattacharjee; Udoy Das; Samia Rahman; Momtazul Arefin Labib; Hasan Murad
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
codecrackers@DravidianLangTech 2025: Sentiment Classification in Tamil and Tulu Code-Mixed Social Media Text Using Machine Learning
Authors: Lalith Kishore V P; Dr G Manikandan; Mohan Raj M A; Keerthi Vasan A; Aravindh M
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Sentiment analysis of code-mixed Dravidian languages has become a major area of concern with increasing volumes of multilingual and code-mixed information across social media. This paper presents the “Seventh Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu”, which was held as part of DravidianLangTech (NAACL-2025). However, sentiment analysis for code-mixed Dravidian languages has received little attention due to challenges such as class imbalance, small sample size, and the informal nature of the code-mixed text. This study applied an SVM-based approach for the sentiment classification of both Tamil and Tulu languages. The SVM model achieved competitive macro-average F1 scores of 0.54 for Tulu and 0.438 for Tamil, demonstrating that traditional machine learning methods can effectively tackle sentiment categorization in code-mixed languages under low-resource settings.
CUET_Ignite@DravidianLangTech 2025: Detection of Abusive Comments in Tamil Text Using Transformer Models
Authors: MD.Mahadi Rahman; Mohammad Minhaj Uddin; Mohammad Shamsul Arefin
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Abusive comment detection in low-resource languages is a challenging task particularly when addressing gender-based abuse. Identifying 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 including Logistic Regression, Random Forest, SVM, CNN, LSTM, BiLSTMand transformer-based models such as mBERT, IndicBERT, XLMRoBERTa and many more. The dataset comprised of Tamil YouTube comments annotated with binary labels, Abusive and NonAbusive capturing explicit abuse, implicit biases and stereotypes. Our experiments demonstrated that XLM-RoBERTa achieved the highest macro F1-score(0.80), highlighting its effectiveness in handling Tamil text. This research contributes to advancing abusive language detection and natural language processing in lowresource languages particularly for addressing gender-based abuse online.
CUET_Absolute_Zero@DravidianLangTech 2025: Detecting AI-Generated Product Reviews in Malayalam and Tamil Language Using Transformer Models
Authors: Anindo Barua; Sidratul Muntaha; Momtazul Arefin Labib; Samia Rahman; Udoy Das; Hasan Murad
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Artificial Intelligence (AI) is opening new doors of learning and interaction. However, it has its share of problems. One major issue is the ability of AI to generate text that resembles human-written text. So, how can we tell apart human-written text from AI-generated text?With this in mind, we have worked on detecting AI-generated product reviews in Dravidian languages, mainly in Malayalam and Tamil. The “Shared Task on Detecting AI-Generated Product Reviews in Dravidian Languages,” held as part of the DravidianLangTech Workshop at NAACL 2025 has provided a dataset categorized into two categories, human-written review and AI-generated review. We have implemented four machine learning models (Random Forest, Support Vector Machine, Decision Tree, and XGBoost), four deep learning models (Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gated Recurrent Unit, and Recurrent Neural Network), and three transformer-based models (AI-Human-Detector, Detect-AI-Text, and E5-Small-Lora-AI-Generated-Detector). We have conducted a comparative study among all the models by training and evaluating each model on the dataset. We have discovered that the transformer, E5-Small-Lora-AI-Generated-Detector, has provided the best result with an F1 score of 0.8994 on the test set ranking 7th position in the Malayalam language. Tamil has a higher token overlap and richer morphology than Malayalam. Thus, we obtained a worse F1 score of 0.5877 ranking 28th position in the Tamil language among all participants in the shared task.
MNLP@DravidianLangTech 2025: Transformers vs. Traditional Machine Learning: Analyzing Sentiment in Tamil Social Media Posts
Authors: Abhay Vishwakarma; Abhinav Kumar
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Sentiment analysis in Natural Language Processing (NLP) aims to categorize opinions in text. In the political domain, understanding public sentiment is crucial for influencing policymaking. Social media platforms like X (Twitter) provide abundant sources of real-time political discourse. This study focuses on political multiclass sentiment analysis of Tamil comments from X, classifying sentiments into seven categories: substantiated, sarcastic, opinionated, positive, negative, neutral, and none of the above. A number of traditional machine learning such as Naive Bayes, Voting Classifier (an ensemble of Decision Tree, SVM, Naive Bayes, K-Nearest Neighbors, and Logistic Regression) and deep learning models such as LSTM, deBERTa, and a hybrid approach combining deBERTa embeddings with an LSTM layer are implemented. The proposed ensemble-based voting classifier achieved best performance among all implemented models with an accuracy of 0.3750, precision of 0.3387, recall of 0.3250, and macro-F1-score of 0.3227.
shimig@DravidianLangTech2025: Stratification of Abusive content on Women in Social Media
Authors: Gersome Shimi; Jerin Mahibha C; Thenmozhi Durairaj
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: The social network is a trending medium for interaction and sharing content globally. The content is sensitive since it can create an impact and change the trends of stakeholder’s thought as well as behavior. When the content is targeted towards women, it may be abusive or non-abusive and the identification is a tedious task. The content posted on social networks can be in English, code mix, or any low-resource language. The shared task Abusive Tamil and Malayalam Text targeting Women on Social Media was conducted as part of DravidianLangTech@NAACL 2025 organized by DravidianLangTech. The task is to identify the content given in Tamil or Malayalam or code mix as abusive or non-abusive. The task is accomplished for the South Indian languages Tamil and Malayalam using pretrained transformer model, BERT base multilingual cased and achieved the accuracy measure of 0.765 and 0.677.
SSNTrio@DravidianLangTech2025: LLM Based Techniques for Detection of Abusive Text Targeting Women
Authors: Mirnalinee T T; J Bhuvana; Avaneesh Koushik; Diya Seshan; Rohan R
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: This study focuses on developing a solution for detecting abusive texts on social media against women in Tamil and Malayalam, two low-resource Dravidian languages in South India. As the usage of social media for communication and idea sharing has increased significantly, these platforms are being used to target and victimize women. Hence an automated solution becomes necessary to screen the huge volume of content generated. This work is part of the shared Task on Abusive Tamil and Malayalam Text targeting Women on Social MediaDravidianLangTech@NAACL 2025. The approach used to tackle this problem involves utilizing LLM based techniques for classifying abusive text. The Macro Average F1-Score for the Tamil BERT model was 0.76 securing the 11th position, while the Malayalam BERT model for Malayalam obtained a score of 0.30 and secured the 33rd rank. The proposed solution can be extended further to incorporate other regional languages as well based on similar techniques.
CUET-NLP_MP@DravidianLangTech 2025: A Transformer and LLM-Based Ensemble Approach for Fake News Detection in Dravidian
Authors: Md Minhazul Kabir; Md. Mohiuddin; Kawsar Ahmed; Mohammed Moshiul Hoque
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Fake news detection is a critical problem in today’s digital age, aiming to classify intentionally misleading or fabricated news content. In this study, we present a transformer and LLM-based ensemble method to address the challenges in fake news detection. We explored various machine learning (ML), deep learning (DL), transformer, and LLM-based approaches on a Malayalam fake news detection dataset. Our findings highlight the difficulties faced by traditional ML and DL methods in accurately detecting fake news, while transformer- and LLM-based ensemble methods demonstrate significant improvements in performance. The ensemble method combining Sarvam-1, Malayalam-BERT, and XLM-R outperformed all other approaches, achieving an F1-score of 89.30% on the given dataset. This accomplishment, which contributed to securing 2nd place in the shared task at DravidianLangTech 2025, underscores the importance of developing effective methods for detecting fake news in Dravidian languages.
CUET-NLP_Big_O@DravidianLangTech 2025: A Multimodal Fusion-based Approach for Identifying Misogyny Memes
Authors: Md. Refaj Hossan; Nazmus Sakib; Md. Alam Miah; Jawad Hossain; Mohammed Moshiul Hoque
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Memes have become one of the main mediums for expressing ideas, humor, and opinions through visual-textual content on social media. The same medium has been used to propagate harmful ideologies, such as misogyny, that undermine gender equality and perpetuate harmful stereotypes. Identifying misogynistic memes is particularly challenging in low-resource languages (LRLs), such as Tamil and Malayalam, due to the scarcity of annotated datasets and sophisticated tools. Therefore, DravidianLangTech@NAACL 2025 launched a Shared Task on Misogyny Meme Detection to identify misogyny memes. For this task, this work exploited an extensive array of models, including machine learning (LR, RF, SVM, and XGBoost), and deep learning (CNN, BiLSTM+CNN, CNN+GRU, and LSTM) are explored to extract textual features, while CNN, BiLSTM + CNN, ResNet50, and DenseNet121 are utilized for visual features.Furthermore, we have explored feature-level and decision-level fusion techniques with several model combinations like MuRIL with ResNet50, MuRIL with BiLSTM+CNN, T5+MuRIL with ResNet50, and mBERT with ResNet50. The evaluation results demonstrated that BERT + ResNet50 performed best, obtaining an F1 score of 0.81716 (Tamil) and were ranked 2nd in the task. The early fusion of MuRIL+ResNet50 showed the highest F1 score of 0.82531 and received a 9th rank in Malayalam.
LexiLogic@DravidianLangTech 2025: Detecting Misogynistic Memes and Abusive Tamil and Malayalam Text Targeting Women on Social Media
Authors: Niranjan Kumar M; Pranav Gupta; Billodal Roy; Souvik Bhattacharyya
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Social media platforms have become a significant medium for communication and expression, but they are also plagued by misogynistic content targeting women. This study focuses on detecting misogyny in memes and abusive textual content in Tamil and Malayalam languages, which are underrepresented in natural language processing research. Leveraging advanced machine learning and deep learning techniques, we developed a system capable of identifying misogynistic memes and abusive text. By addressing cultural and linguistic nuances, our approach enhances detection accuracy and contributes to safer online spaces for women. This work also serves as a foundation for expanding misogyny detection to other low-resource languages, fostering inclusivity and combating online abuse effectively.This paper presents our work on detecting misogynistic memes and abusive Tamil and Malayalam text targeting women on social media platforms. Leveraging the pretrained models l3cube-pune/tamil-bert and l3cube-pune/malayalam-bert, we explored various data cleaning and augmentation strategies to enhance detection performance. The models were fine-tuned on curated datasets and evaluated using accuracy, F1-score, precision, and recall. The results demonstrated significant improvements with our cleaning and augmentation techniques, yielding robust performance in detecting nuanced and culturally-specific abusive content.Our model achieved macro F1 scores of 77.83/78.24 on L3Cube-Bert-Tamil and 78.16/77.01 on L3Cube-Bert-Malayalam, ranking 3rd and 4th on the leaderboard. For the misogyny task, we obtained 83.58/82.94 on L3Cube-Bert-Malayalam and 73.16/73.8 on L3Cube-Bert-Tamil, placing 9th in both. These results highlight our model’s effectiveness in low-resource language classification.