ymoslem/acl-abstracts huggingface dataset:
Incepto@DravidianLangTech 2025: Detecting Abusive Tamil and Malayalam Text Targeting Women on YouTube (2025)

This study introduces a novel multilingualmodel designed to effectively address the challenges of detecting abusive content in low resource, code-mixed languages, where limiteddata availability and the interplay of mixed languages, leading to complex linguistic phenomena, create significant hurdles in developingrobust machine learning models. By leveraging transfer learning techniques and employingmulti-head attention mechanisms, our modeldemonstrates impressive performance in detecting abusive content in both Tamil and Malayalam datasets. On the Tamil dataset, our teamachieved a macro F1 score of 0.7864, whilefor the Malayalam dataset, a macro F1 score of0.7058 was attained. These results highlight theeffectiveness of our multilingual approach, delivering strong performance in Tamil and competitive results in Malayalam.
Eureka-CIOL@DravidianLangTech 2025: Using Customized BERTs for Sentiment Analysis of Tamil Political Comments (2025)

Sentiment analysis on social media platforms plays a crucial role in understanding public opinion and the decision-making process on political matters. As a significant number of individuals express their views on social media, analyzing these opinions is essential for monitoring political trends and assessing voter sentiment. However, sentiment analysis for low-resource languages, such as Tamil, presents considerable challenges due to the limited availability of annotated datasets and linguistic complexities. To address this gap, we utilize a novel dataset encompassing seven sentiment classes, offering a unique opportunity to explore sentiment variations in Tamil political discourse. In this study, we evaluate multiple pre-trained models from the Hugging Face library and experiment with various hyperparameter configurations to optimize model performance. Our findings aim to contribute to the development of more effective sentiment analysis tools tailored for low-resource languages, ultimately empowering Tamil-speaking communities by providing deeper insights into their political sentiments. Our full experimental codebase is publicly available at: ciol-researchlab/NAACL25-Eureka-Sentiment-Analysis-Tamil
Akatsuki-CIOL@DravidianLangTech 2025: Ensemble-Based Approach Using Pre-Trained Models for Fake News Detection in Dravidian Languages (2025)

The widespread spread of fake news on social media poses significant challenges, particularly for low-resource languages like Malayalam. The accessibility of social platforms accelerates misinformation, leading to societal polarization and poor decision-making. Detecting fake news in Malayalam is complex due to its linguistic diversity, code-mixing, and dialectal variations, compounded by the lack of large labeled datasets and tailored models. To address these, we developed a fine-tuned transformer-based model for binary and multiclass fake news detection. The binary classifier achieved a macro F1 score of 0.814, while the multiclass model, using multimodal embeddings, achieved a score of 0.1978. Our system ranked 14th and 11th in the shared task competition, highlighting the need for specialized techniques in underrepresented languages. Our full experimental codebase is publicly available at: ciol-researchlab/NAACL25-Akatsuki-Fake-News-Detection.
RMKMavericks@DravidianLangTech 2025: Tackling Abusive Tamil and Malayalam Text Targeting Women: A Linguistic Approach (2025)

Social media abuse of women is a widespread problem, especially in regional languages like Tamil and Malayalam, where there are few tools for automated identification. The use of machine learning methods to detect abusive messages in several languages is examined in this work. An external dataset was used to train a Support Vector Machine (SVM) model for Tamil, which produced an F1 score of 0.6196. Using the given dataset, a Multinomial Naive Bayes (MNB) model was trained for Malayalam, obtaining an F1 score of 0.6484. Both models processed and analyzed textual input efficiently by using TF-IDF vectorization for feature extraction. This method shows the ability to solve the linguistic diversity and complexity of abusive language identification by utilizing language-specific datasets and customized algorithms. The results highlight how crucial it is to use focused machine learning techniques to make online spaces safer for women, especially when speaking minority languages.
RMKMavericks@DravidianLangTech 2025: Emotion Mining in Tamil and Tulu Code-Mixed Text: Challenges and Insights (2025)

Sentiment analysis in code-mixed social media comments written in Tamil and Tulu presents unique challenges due to grammatical inconsistencies, code-switching, and the use of non-native scripts. To address these complexities, we employ pre-processing techniques for text cleaning and evaluate machine learning models tailored for sentiment detection. Traditional machine learning methods combined with feature extraction strategies, such as TF- IDF, are utilized. While logistic regression demonstrated reasonable performance on the Tamil dataset, achieving a macro F1 score of 0.44, support vector machines (SVM) outperformed logistic regression on the Tulu dataset with a macro F1 score of 0.54. These results demonstrate the effectiveness of traditional approaches, particularly SVM, in handling low- resource, multilingual data, while also high- lighting the need for further refinement to improve performance across underrepresented sentiment classes.
JAS@DravidianLangTech 2025: Abusive Tamil Text targeting Women on Social Media (2025)

This paper presents our submission for Abusive Comment Detection in Tamil - DravidianLangTech@NAACL 2025. The aim is to classify whether a given comment is abusive towards women. Google`s MuRIL (Khanujaet al., 2021), a transformer-based multilingual model, is fine-tuned using the provided dataset to build the classification model. The datasetis preprocessed, tokenised, and formatted for model training. The model is trained and evaluated using accuracy, F1-score, precision, andrecall. Our approach achieved an evaluation accuracy of 77.76{\%} and an F1-score of 77.65{\%}. The lack of large, high-quality datasets forlow-resource languages has also been acknowledged.
Team-Risers@DravidianLangTech 2025: AI-Generated Product Review Detection in Dravidian Languages Using Transformer-Based Embeddings (2025)

Online product reviews influence customer choices and company reputations. However, companies can counter negative reviews by generating fake reviews that portray their products positively. These fake reviews lead to legal disputes and concerns, particularly because AI detection tools are limited in low-resource languages such as Tamil and Malayalam. To address this, we use machine learning and deep learning techniques to identify AI-generated reviews. We utilize Tamil BERT and Malayalam BERT in the embedding layer to extract contextual features. These features are sent to a Feedforward Neural Network (FFN) with softmax to classify reviews as AI-generated or not. The performance of the model is evaluated on the dataset. The results show that the transformer-based embedding achieves a better accuracy of 95.68{\textbackslash}{\%} on Tamil data and an accuracy of 88.75{\textbackslash}{\%} on Malayalam data.
NLPopsCIOL@DravidianLangTech 2025: Classification of Abusive Tamil and Malayalam Text Targeting Women Using Pre-trained Models (2025)

Hate speech detection in multilingual and code-mixed contexts remains a significant challenge due to linguistic diversity and overlapping syntactic structures. This paper presents a study on the detection of hate speech in Tamil and Malayalam using transformer-based models. Our goal is to address underfitting and develop effective models for hate speech classification. We evaluate several pre-trained models, including MuRIL and XLM-RoBERTa, and show that fine-tuning is crucial for better performance. The test results show a Macro-F1 score of 0.7039 for Tamil and 0.6402 for Malayalam, highlighting the promise of these models with further improvements in fine-tuning. We also discuss data preprocessing techniques, model implementations, and experimental findings. Our full experimental codebase is publicly available at: github.com/ciol-researchlab/NAACL25-NLPops-Classification-Abusive-Text.
AiMNLP@DravidianLangTech 2025: Unmask It! AI-Generated Product Review Detection in Dravidian Languages (2025)

The rise of Generative AI has led to a surge in AI-generated reviews, often posing a serious threat to the credibility of online platforms. Reviews serve as the primary source of information about products and services. Authentic reviews play a vital role in consumer decision-making. The presence of fabricated content misleads consumers, undermines trust and facilitates potential fraud in digital marketplaces. This study focuses on detecting AI-generated product reviews in Tamil and Malayalam, two low-resource languages where research in this domain is relatively under-explored. We worked on a range of approaches - from traditional machine learning methods to advanced transformer-based models such as Indic-BERT, IndicSBERT, MuRIL, XLM-RoBERTa and Malayalam-BERT. Our findings highlight the effectiveness of leveraging the state-of-the-art transformers in accurately identifying AI-generated content, demonstrating the potential in enhancing the detection of fake reviews in low-resource language settings.
byteSizedLLM@DravidianLangTech 2025: Fake News Detection in Dravidian Languages Using Transliteration-Aware XLM-RoBERTa and Transformer Encoder-Decoder (2025)

This study addresses the challenge of fake news detection in code-mixed and transliterated text, focusing on a multilingual setting with significant linguistic variability. A novel approach is proposed, leveraging a fine-tuned multilingual transformer model trained using Masked Language Modeling on a dataset that includes original, fully transliterated, and partially transliterated text. The fine-tuned embeddings are integrated into a custom transformer classifier designed to capture complex dependencies in multilingual sequences. The system achieves state-of-the-art performance, demonstrating the effectiveness of combining transliteration-aware fine-tuning with robust transformer architectures to handle code-mixed and resource-scarce text, providing a scalable solution for multilingual natural language processing tasks.
byteSizedLLM@DravidianLangTech 2025: Fake News Detection in Dravidian Languages Using Transliteration-Aware XLM-RoBERTa and Attention-BiLSTM (2025)

This research introduces an innovative Attention BiLSTM-XLM-RoBERTa model for tackling the challenge of fake news detection in Malayalam datasets. By fine-tuning XLM-RoBERTa with Masked Language Modeling (MLM) on transliteration-aware data, the model effectively bridges linguistic and script diversity, seamlessly integrating native, Romanized, and mixed-script text. Although most of the training data is monolingual, the proposed approach demonstrates robust performance in handling diverse script variations. Achieving a macro F1-score of 0.5775 and securing top rankings in the shared task, this work highlights the potential of multilingual models in addressing resource-scarce language challenges and sets a foundation for future advancements in fake news detection.
byteSizedLLM@DravidianLangTech 2025: Multimodal Hate Speech Detection in Malayalam Using Attention-Driven BiLSTM, Malayalam-Topic-BERT, and Fine-Tuned Wav2Vec 2.0 (2025)

This research presents a robust multimodal framework for hate speech detection in Malayalam, combining fine-tuned Wav2Vec 2.0, Malayalam-Doc-Topic-BERT, and an Attention-Driven BiLSTM architecture. The proposed approach effectively integrates acoustic and textual features, achieving a macro F1-score of 0.84 on the Malayalam test set. Fine-tuning Wav2Vec 2.0 on Malayalam speech data and leveraging Malayalam-Doc-Topic-BERT significantly improved performance over prior methods using openly available models. The results highlight the potential of language-specific models and advanced multimodal fusion techniques for addressing nuanced hate speech categories, setting the stage for future work on Dravidian languages like Tamil and Telugu.
byteSizedLLM@DravidianLangTech 2025: Detecting AI-Generated Product Reviews in Dravidian Languages Using XLM-RoBERTa and Attention-BiLSTM (2025)

This study presents a hybrid model integrating TamilXLM-RoBERTa and MalayalamXLM-RoBERTa with BiLSTM and attention mechanisms to classify AI-generated and human-written product reviews in Tamil and Malayalam. The model employs a transliteration-based fine-tuning strategy, effectively handling native, Romanized, and mixed-script text. Despite being trained on a relatively small portion of data, our approach demonstrates strong performance in distinguishing AI-generated content, achieving competitive macro F1 scores in the DravidianLangTech 2025 shared task. The proposed method showcases the effectiveness of multilingual transformers and hybrid architectures in tackling low-resource language challenges.
byteSizedLLM@DravidianLangTech 2025: Abusive Tamil and Malayalam Text targeting Women on Social Media Using XLM-RoBERTa and Attention-BiLSTM (2025)

This research investigates abusive comment detection in Tamil and Malayalam, focusing on code-mixed, multilingual social media text. A hybrid Attention BiLSTM-XLM-RoBERTa model was utilized, combining fine-tuned embeddings, sequential dependencies, and attention mechanisms. Despite computational constraints limiting fine-tuning to a subset of the AI4Bharath dataset, the model achieved competitive macro F1-scores, ranking 6th for both Tamil and Malayalam datasets with minor performance differences. The results emphasize the potential of multilingual transformers and the need for further advancements, particularly in addressing linguistic diversity, transliteration complexity, and computational limitations.
byteSizedLLM@DravidianLangTech 2025: Multimodal Misogyny Meme Detection in Low-Resource Dravidian Languages Using Transliteration-Aware XLM-RoBERTa, ResNet-50, and Attention-BiLSTM (2025)

Detecting misogyny in memes is challenging due to their multimodal nature, especially in low-resource languages like Tamil and Malayalam. This paper presents our work in the Misogyny Meme Detection task, utilizing both textual and visual features. We propose an Attention-Driven BiLSTM-XLM-RoBERTa-ResNet model, combining a transliteration-aware fine-tuned XLM-RoBERTa for text analysis and ResNet-50 for image feature extraction. Our model achieved Macro-F1 scores of 0.8805 for Malayalam and 0.8081 for Tamil, demonstrating competitive performance. However, challenges such as class imbalance and domain-specific image representation persist. Our findings highlight the need for better dataset curation, task-specific fine-tuning, and advanced fusion techniques to enhance multimodal hate speech detection in Dravidian languages.
byteSizedLLM@DravidianLangTech 2025: Sentiment Analysis in Tamil Using Transliteration-Aware XLM-RoBERTa and Attention-BiLSTM (2025)

This study investigates sentiment analysis in code-mixed Tamil-English text using an Attention BiLSTM-XLM-RoBERTa model, combining multilingual embeddings with sequential context modeling to enhance classification performance. The model was fine-tuned using masked language modeling and trained with an attention-based BiLSTM classifier to capture sentiment patterns in transliterated and informal text. Despite computational constraints limiting pretraining, the approach achieved a Macro f1 of 0.5036 and ranked first in the competition. The model performed best on the Positive class, while Mixed Feelings and Unknown State showed lower recall due to class imbalance and ambiguity. Error analysis reveals challenges in handling non-standard transliterations, sentiment shifts, and informal language variations in social media text. These findings demonstrate the effectiveness of transformer-based multilingual embeddings and sequential modeling for sentiment classification in code-mixed text.
SSNCSE@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages (2025)

Hate speech detection is a serious challenge due to the different digital media communication, particularly in low-resource languages. This research focuses on the problem of multimodal hate speech detection by incorporating both textual and audio modalities. In the context of social media platforms, hate speech is conveyed not only through text but also through audios, which may further amplify harmful content. In order to manage the issue, we provide a multiclass classification model that influences both text and audio features to detect and categorize hate speech in low-resource languages. The model uses machine learning models for text analysis and audio processing, allowing it to efficiently capture the complex relationships between the two modalities. Class weight mechanism involves avoiding overfitting. The prediction has been finalized using the majority fusion technique. Performance is measured using a macro average F1 score metric. Three languages{---}Tamil, Malayalam, and Telugu{---}have the optimal F1-scores, which are 0.59, 0.52, and 0.33.
Bridging Linguistic Complexity: Sentiment Analysis of Tamil Code-Mixed Text Using Meta-Model (2025)

Sentiment analysis in code-mixed languages poses significant challenges due to the complex nature of mixed-language text. This study explores sentiment analysis on Tamil code-mixed text using deep learning models such as Long Short-Term Memory (LSTM), hybrid models like Convolutional Neural Network (CNN) + Gated Recurrent Unit (GRU) and LSTM + GRU, along with meta-models including Logistic Regression, Random Forest, and Decision Tree. The LSTM+GRU hybrid model achieved an accuracy of 0.31, while the CNN+GRU hybrid model reached 0.28. The Random Forest meta-model demonstrated exceptional performance on the development set with an accuracy of 0.99. However, its performance dropped significantly on the test set, achieving an accuracy of 0.1333. The study results emphasize the potential of meta-model-based classification for improving performance in NLP tasks.
YenCS@DravidianLangTech 2025: Integrating Hybrid Architectures for Fake News Detection in Low-Resource Dravidian Languages (2025)

Detecting fake news in under-resourced Dravidian languages is a rigorous task due to the scarcity of annotated datasets and the intricate nature of code-mixed text. This study tackles these issues by employing advanced machine learning techniques for two key classification tasks, the first task involves binary classification achieving a macro-average F1-score of 0.792 using a hybrid fusion model that integrates Bidirectional Recurrent Neural Network (Bi-RNN) and Long Short-Term Memory (LSTM)-Recurrent Neural Network (RNN) with weighted averaging. The second task focuses on fine-grained classification, categorizing news where an LSTM-GRU hybrid model attained a macro-average F1-score of 0.26. These findings highlight the effectiveness of hybrid models in improving fake news detection for under-resourced languages. Additionally, this study provides a foundational framework that can be adapted to address similar challenges in other under-resourced languages, emphasizing the need for further research in this area.
Overview of the Shared Task on Multimodal Hate Speech Detection in Dravidian languages: DravidianLangTech@NAACL 2025 (2025)

The detection of hate speech in social media platforms is very crucial these days. This is due to its adverse impact on mental health, social harmony, and online safety. This paper presents the overview of the shared task on Multimodal Hate Speech Detection in Dravidian Languages organized as part of DravidianLangTech@NAACL 2025. The task emphasizes detecting hate speech in social media content that combines speech and text. Here, we focus on three low-resource Dravidian languages: Malayalam, Tamil, and Telugu. Participants were required to classify hate speech in three sub-tasks, each corresponding to one of these languages. The dataset was curated by collecting speech and corresponding text from YouTube videos. Various machine learning and deep learning-based models, including transformer-based architectures and multimodal frameworks, were employed by the participants. The submissions were evaluated using the macro F1 score. Experimental results underline the potential of multimodal approaches in advancing hate speech detection for low-resource languages. Team SSNTrio achieved the highest F1 score in Malayalam and Tamil of 0.7511 and 0.7332, respectively. Team lowes scored the best F1 score of 0.3817 in the Telugu sub-task.
Overview of the Shared Task on Detecting AI Generated Product Reviews in Dravidian Languages: DravidianLangTech@NAACL 2025 (2025)

The detection of AI-generated product reviews is critical due to the increased use of large language models (LLMs) and their capability to generate convincing sentences. The AI-generated reviews can affect the consumers and businesses as they influence the trust and decision-making. This paper presents the overview of the shared task on Detecting AI-generated product reviews in Dravidian Languages{\textquotedblright} organized as part of DravidianLangTech@NAACL 2025. This task involves two subtasks{---}one in Malayalam and another in Tamil, both of which are binary classifications where a review is to be classified as human-generated or AI-generated. The dataset was curated by collecting comments from YouTube videos. Various machine learning and deep learning-based models ranging from SVM to transformer-based architectures were employed by the participants.
Girma@DravidianLangTech 2025: Detecting AI Generated Product Reviews (2025)

The increasing prevalence of AI-generated content, including fake product reviews, poses significant challenges in maintaining authenticity and trust in e-commerce systems. While much work has focused on detecting such reviews in high-resource languages, limited attention has been given to low-resource languages like Malayalam and Tamil. This study aims to address this gap by developing a robust framework to identify AI-generated product reviews in these languages. We explore a BERT-based approach for this task. Our methodology involves fine-tuning a BERT-based model specifically on Malayalam and Tamil datasets. The experiments are conducted using labeled datasets that contain a mix of human-written and AI-generated reviews. Performance is evaluated using the macro F1 score. The results show that the BERT-based model achieved a macro F1 score of 0.6394 for Tamil and 0.8849 for Malayalam. Preliminary results indicate that the BERT-based model performs significantly better for Malayalam than for Tamil in terms of the average Macro F1 score, leveraging its ability to capture the complex linguistic features of these languages. Finally, we open the source code of the implementation in the GitHub repository: AI-Generated-Product-Review-Code
Beyond\_Tech@DravidianLangTech 2025: Political Multiclass Sentiment Analysis using Machine Learning and Neural Network (2025)

Research on political feeling is essential for comprehending public opinion in the digital age, as social media and news platforms are often the sites of discussions. To categorize political remarks into sentiments like positive, negative, neutral, opinionated, substantiated, and sarcastic, this study offers a multiclass sentiment analysis approach. We trained models, such as Random Forest and a Feedforward Neural Network, after preprocessing and feature extraction from a large dataset of political texts using Natural Language Processing approaches. The Random Forest model, which was great at identifying more complex attitudes like sar casm and opinionated utterances, had the great est accuracy of 84{\%}, followed closely by the Feedforward Neural Network model, which had 83{\%}. These results highlight how well political discourse can be analyzed by combining deep learning and traditional machine learning techniques. There is also room for improvement by adding external metadata and using sophisticated models like BERT for better sentiment classification.