Text-based hate speech has been prevalent and is usually used to incite hostility and violence. Detecting this content becomes imperative, yet the task is challenging, particularly for low-resource languages in the Devanagari script, which must have the extensive labeled datasets required for effective machine learning. To address this, a shared task has been organized for identifying hate speech targets in Devanagari-script text. The task involves classifying targets such as individuals, organizations, and communities and identifying different languages within the script. We have explored several machine learning methods such as LR, SVM, MNB, and Random Forest, deep learning models using CNN, BiLSTM, GRU, CNN+BiLSTM, and transformer-based models like Indic-BERT, m-BERT, Verta-BERT, XLM-R, and MuRIL. The CNN with BiLSTM yielded the best performance (F1-score of 0.9941), placing the team 13th in the competition for script identification. Furthermore, the fine-tuned MuRIL-BERT model resulted in an F1 score of 0.6832, ranking us 4th for detecting hate speech targets.

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.

The rapid growth of digital platforms and social media has significantly contributed to spreading fake news, posing serious societal challenges. While extensive research has been conducted on detecting fake news in high-resource languages (HRLs) such as English, relatively little attention has been given to low-resource languages (LRLs) like Malayalam due to insufficient data and computational tools. To address this challenge, the DravidianLangTech 2025 workshop organized a shared task on fake news detection in Dravidian languages. The task was divided into two sub-tasks, and our team participated in Task 1, which focused on classifying social media texts as original or fake. We explored a range of machine learning (ML) techniques, including Logistic Regression (LR), Multinomial Naïve Bayes (MNB), and Support Vector Machines (SVM), as well as deep learning (DL) models such as CNN, BiLSTM, and a hybrid CNN+BiLSTM. Additionally, this work examined several transformer-based models, including m-BERT, Indic-BERT, XLM-Roberta, and MuRIL-BERT, to exploit the task. Our team achieved 6th place in Task 1, with MuRIL-BERT delivering the best performance, achieving an F1 score of 0.874.