Stance and target detection in multimodal political memes presents notable challenges in low-resource and highly imbalanced settings.This task is based on the Malayalam dataset from the DravidianLangTech 2026 Shared Task(500 samples with a 95.4:4.6 stance imbalance).The primary challenges stem from linguistic variability and visually complex meme formats,which hinder accurate text extraction and effective multimodal alignment. A lightweight yet high-performing multimodal framework is proposed that integrates bilingual OCR, a Vision Transformer (ViT), and IndicBERT to learn complementary visual and textual representations. A gated fusion mechanism effectivelycombines multimodal features, while asymmetric loss weighting and post-training threshold optimization address extreme class imbalance. The methodology achieves a Weighted F1-score of 0.9535 for stance detection and 0.5283 for target identification, demonstrating strong robustness and generalization under realistic multimodal constraints.

The fast-growing number of content in Tamil in social media has led to increasing abusive and gender-directed hate speech in online platforms. Detecting abusive content written in Tamil is relatively difficult owing to the complex morphological structure of Tamil language, its dialects, transliteration, and contextualized usage. In this study, the use of transformer-based pretrained language models in detecting abusive content in Tamil was explored. Five transformer-based models—mBERT, MuRIL, XLM-RoBERTa, IndicBERT, and Tamil-BERT—were fine-tuned and tested using DravidianLangTech 2026 shared task dataset. The experimental results show that the best-performing model was Tamil-BERT with an accuracy rate of 80.72% owing to Tamil-specific pretraining and better morphological analysis capabilities. Our system ranks 5th at the leaderboard of the DravidianLangTech 2026 shared task challenge. The source code and fine-tuned models are opensource and publicly accessible.

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\% on Tamil data and an accuracy of 88.75\% on Malayalam data.

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.