GenAI4ELab/papercli-papers huggingface dataset:
MNLP@DravidianLangTech 2025: Transformer-based Multimodal Framework for Misogyny Meme Detection
Authors: Shraddha Chauhan; Abhinav Kumar
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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
Authors: Harish Vijay V; Ippatapu Venkata Srichandra; Pathange Omkareshwara Rao; Premjith B
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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)
KEC_TECH_TITANS@DravidianLangTech 2025: Abusive Text Detection in Tamil and Malayalam Social Media Comments Using Machine Learning
Authors: Malliga Subramanian; Kogilavani Shanmugavadivel; Deepiga P; Dharshini S; Ananthakumar S; Praveenkumar C
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Social media platforms have become a breeding ground for hostility and toxicity, with abusive language targeting women becoming a pervasive issue. This paper addresses the detection of abusive content in Tamil and Malayalam social media comments using machine learning models. We experimented with GRU, LSTM, Bidirectional LSTM, CNN, FastText, and XGBoost models, evaluating their performance on a code-mixed dataset of Tamil and Malayalam comments collected from YouTube. Our findings demonstrate that FastText and CNN models yielded the best performance among the evaluated classifiers, achieving F1-scores of 0.73 each. This study contributes to the ongoing research on abusive text detection for under-resourced languages and highlights the need for robust, scalable solutions to combat online toxicity.
F2 (FutureFiction): Detection of Fake News on Futuristic Technology
Authors: Msvpj Sathvik; Venkatesh Velugubantla; Ravi Teja Potla
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: There is widespread of misinformation on futuristic technology and society. To accurately detect such news, the algorithms require up-to-date knowledge. The Large Language Models excel in the NLP but cannot retrieve the ongoing events or innovations. For example, GPT and it’s variants are restricted till the knowledge of 2021. We introduce a new methodology for the identification of fake news pertaining to futuristic technology and society. Leveraging the power of Google Knowledge, we enhance the capabilities of the GPT-3.5 language model, thereby elevating its performance in the detection of misinformation. The proposed framework exhibits superior efficacy compared to established baselines with the accuracy of 81.04%. Moreover, we propose a novel dataset consisting of fake news in three languages English, Telugu and Tenglish of around 21000 from various sources.
JustATalentedTeam@DravidianLangTech 2025: A Study of ML and DL approaches for Sentiment Analysis in Code-Mixed Tamil and Tulu Texts
Authors: Ponsubash Raj R; Paruvatha Priya B; Bharathi B
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
KEC_TECH_TITANS@DravidianLangTech 2025:Sentiment Analysis for Low-Resource Languages: Insights from Tamil and Tulu using Deep Learning and Machine Learning Models
Authors: Malliga Subramanian; Kogilavani Shanmugavadivel; Dharshini S; Deepiga P; Praveenkumar C; Ananthakumar S
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Sentiment analysis in Dravidian languages like Tamil and Tulu presents significant challenges due to their linguistic diversity and limited resources for natural language processing (NLP). This study explores sentiment classification for Tamil and Tulu, focusing on the complexities of handling both languages, which differ in script, grammar, and vocabulary. We employ a variety of machine learning and deep learning techniques, including traditional models like Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), as well as advanced transformer-based models like BERT and multilingual BERT (mBERT). A key focus of this research is to evaluate the performance of these models on sentiment analysis tasks, considering metrics such as accuracy, precision, recall, and F1-score. The results show that transformer-based models, particularly mBERT, significantly outperform traditional machine learning models in both Tamil and Tulu sentiment classification. This study also highlights the need for further research into addressing challenges like language-specific nuances, dataset imbalance, and data augmentation techniques for improved sentiment analysis in under-resourced languages like Tamil and Tulu.
Code_Conquerors@DravidianLangTech 2025: Multimodal Misogyny Detection in Dravidian Languages Using Vision Transformer and BERT
Authors: Pathange Omkareshwara Rao; Harish Vijay V; Ippatapu Venkata Srichandra; Neethu Mohan; Sachin Kumar S
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
YenLP_CS@DravidianLangTech 2025: Sentiment Analysis on Code-Mixed Tamil-Tulu Data Using Machine Learning and Deep Learning Models
Authors: Raksha Adyanthaya; Rathnakara Shetty P
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
LinguAIsts@DravidianLangTech 2025: Abusive Tamil and Malayalam Text targeting Women on Social Media
Authors: Dhanyashree G; Kalpana K; Lekhashree A; Arivuchudar K; Arthi R; Bommineni Sahitya; Pavithra J; Sandra Johnson
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Social media sites are becoming crucial sites for communication and interaction, yet they are increasingly being utilized to commit gender-based abuse, with horrific, harassing, and degrading comments targeted at women. This paper tries to solve the common issue of women being subjected to abusive language in two South Indian languages, Malayalam and Tamil. To find explicit abuse, implicit bias, preconceptions, and coded language, we were given a set of YouTube comments labeled Abusive and Non-Abusive. To solve this problem, we applied and compared different machine learning models, i.e., Support Vector Machines (SVM), Logistic Regression (LR), and Naive Bayes classifiers, to classify comments into the given categories. The models were trained and validated using the given dataset to achieve the best performance with respect to accuracy and macro F1 score. The solutions proposed aim to make robust content moderation systems that can detect and prevent abusive language, ensuring safer online environments for women.
KEC-Elite-Analysts@DravidianLangTech 2025: Deciphering Emotions in Tamil-English and Code-Mixed Social Media Tweets
Authors: Malliga Subramanian; Aruna A; Anbarasan T; Amudhavan M; Jahaganapathi S; Kogilavani Shanmugavadivel
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
Cyber Protectors@DravidianLangTech 2025: Abusive Tamil and Malayalam Text Targeting Women on Social Media using FastText
Authors: Rohit Vp; Madhav M; Ippatapu Venkata Srichandra; Neethu Mohan; Sachin Kumar S
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Social media has transformed communication, but it has opened new ways for women to be abused. Because of complex morphology, large vocabulary, and frequent code-mixing of Tamil and Malayalam, it might be especially challenging to identify discriminatory text in linguistically diverse settings. Because traditional moderation systems frequently miss these linguistic subtleties, gendered abuse in many forms—from outright threats to character insults and body shaming—continues. In addition to examining the sociocultural characteristics of this type of harassment on social media, this study compares the effectiveness of several Natural Language Processing (NLP) models, such as FastText, transformer-based architectures, and BiLSTM. Our results show that FastText achieved an macro f1 score of 0.74 on the Tamil dataset and 0.64 on the Malayalam dataset, outperforming the Transformer model which achieved a macro f1 score of 0.62 and BiLSTM achieved 0.57. By addressing the limitations of existing moderation techniques, this research underscores the urgent need for language-specific AI solutions to foster safer digital spaces for women.
LinguAIsts@DravidianLangTech 2025: Misogyny Meme Detection using multimodel Approach
Authors: Arthi R; Pavithra J; Dr G Manikandan; Lekhashree A; Dhanyashree G; Bommineni Sahitya; Arivuchudar K; Kalpana K
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Memes often disseminate misogynistic material, which nurtures gender discrimination and stereotyping. While it is an effective tool of communication, social media has also provided a fertile ground for online abuse. This vital issue in the multilingual and multimodal setting is tackled by the Misogyny Meme Detection Shared Task. Our method employs advanced NLP techniques and machine learning models to classify memes in Malayalam and Tamil, two low-resource languages. Preprocessing of text includes tokenization, lemmatization, and stop word removal. Features are then extracted using TF-IDF. With the best achievable hyperparameters, along with the SVM model, our system provided very promising outcomes and ranked 9th among the systems competing in the Tamil task with a 0.71259 F1-score, and ranked 15th with an F1-score of 0.68186 in the Malayalam taks. With this research work, it would be established how important AI-based solutions are toward stopping online harassment and developing secure online spaces.
CUET_Agile@DravidianLangTech 2025: Fine-tuning Transformers for Detecting Abusive Text Targeting Women from Tamil and Malayalam Texts
Authors: Tareque Md Hanif; Md Rashadur Rahman
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
Necto@DravidianLangTech 2025: Fine-tuning Multilingual MiniLM for Text Classification in Dravidian Languages
Authors: Livin Nector Dhasan
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: This paper explores the application of a fine-tuned Multilingual MiniLM model for various binary text classification tasks, including AI-generated product review detection, abusive language targeting woman detection, and fake news detection in the Dravidian languages Tamil and Malayalam. This work was done as part of submissions to shared tasks organized by DravidianLangTech@NAACL 2025. The model was fine-tuned using both Tamil and Malayalam datasets, and its performance was evaluated across different tasks using macro F1-score. The results indicate that this model produces performance that is very close to the best F1 score reported by other teams. An investigation is conducted on the AI-generated product review dataset and the findings are reported.
CUET-823@DravidianLangTech 2025: Shared Task on Multimodal Misogyny Meme Detection in Tamil Language
Authors: Arpita Mallik; Ratnajit Dhar; Udoy Das; Momtazul Arefin Labib; Samia Rahman; Hasan Murad
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
Hermes@DravidianLangTech 2025: Sentiment Analysis of Dravidian Languages using XLM-RoBERTa
Authors: Emmanuel George P; Ashiq Firoz; Madhav Murali; Siranjeevi Rajamanickam; Balasubramanian Palani
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: 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.
SSNTrio@DravidianLangTech 2025: Identification of AI Generated Content in Dravidian Languages using Transformers
Authors: J Bhuvana; Mirnalinee T T; Rohan R; Diya Seshan; Avaneesh Koushik
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: The increasing prevalence of AI-generated content has raised concerns about the authenticity and reliability of online reviews, particularly in resource-limited languages like Tamil and Malayalam. This paper presents an approach to the Shared Task on Detecting AI-generated Product Reviews in Dravidian Languages at NAACL2025, which focuses on distinguishing AI-generated reviews from human-written ones in Tamil and Malayalam. Several transformer-based models, including IndicBERT, RoBERTa, mBERT, and XLM-R, were evaluated, with language-specific BERT models for Tamil and Malayalam demonstrating the best performance. The chosen methodologies were evaluated using Macro Average F1 score. In the rank list released by the organizers, team SSNTrio, achieved ranks of 3rd and 29th for the Malayalam and Tamil datasets with Macro Average F1 Scores of 0.914 and 0.598 respectively.
SSNTrio@DravidianLangTech 2025: Sentiment Analysis in Dravidian Languages using Multilingual BERT
Authors: J Bhuvana; Mirnalinee T T; Diya Seshan; Rohan R; Avaneesh Koushik
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: This paper presents an approach to sentiment analysis for code-mixed Tamil-English and Tulu-English datasets as part of the DravidianLangTech@NAACL 2025 shared task. Sentiment analysis, the process of determining the emotional tone or subjective opinion in text, has become a critical tool in analyzing public sentiment on social media platforms. The approach discussed here uses multilingual BERT (mBERT) fine-tuned on the provided datasets to classify sentiment polarity into various predefined categories: for Tulu, the categories were positive, negative, not_tulu, mixed, and neutral; for Tamil, the categories were positive, negative, unknown, mixed_feelings, and neutral. The mBERT model demonstrates its effectiveness in handling sentiment analysis for codemixed and resource-constrained languages by achieving an F1-score of 0.44 for Tamil, securing the 6th position in the ranklist; and 0.56 for Tulu, ranking 5th in the respective task.