With the exponential growth of social media usage, the prevalence of abusive language targeting women has become a pressing issue, particularly in low-resource languages (LRLs) like Tamil and Malayalam. This study is part of the shared task at DravidianLangTech@NAACL 2025, which focuses on detecting abusive comments in Tamil social media content. The provided dataset consists of binary-labeled comments (Abusive or Non-Abusive), gathered from YouTube, reflecting explicit abuse, implicit bias, stereotypes, and coded language. We developed and evaluated multiple models for this task, including traditional machine learning algorithms (Logistic Regression, Support Vector Machine, Random Forest Classifier, and Multinomial Naive Bayes), deep learning models (CNN, BiLSTM, and CNN+BiLSTM), and transformer-based architectures (DistilBERT, Multilingual BERT, XLM-RoBERTa), and fine-tuned variants of these models. Our best-performing model, Multilingual BERT, achieved a weighted F1-score of 0.7203, ranking 19 in the competition.

False narratives that manipulate racial tensions are increasingly prevalent on social media, often blending languages and cultural references to enhance reach and believability. Among them, racial hoaxes produce unique harm by fabricating events targeting specific communities, social division and fueling misinformation. This paper presents a novel approach to detecting racial hoaxes in code-mixed Hindi-English social media data. Using a carefully constructed training pipeline, we have fine-tuned the XLM-RoBERTa-base multilingual transformer for training the shared task data. Our approach has incorporated task-specific preprocessing, clear methodology, and extensive hyperparameter tuning. After developing our model, we tested and evaluated it on the LT-EDI@LDK 2025 shared task dataset. Our system achieved the highest performance among all the international participants with an F1-score of 0.75, ranking 1st on the official leaderboard.

Memes, combining visual and textual elements, have emerged as a prominent medium for both expression and the spread of harmful ideologies, including misogyny. To address this issue in Chinese online content, we present a multimodal framework for misogyny meme detection as part of the LT-EDI@LDK 2025 Shared Task. Our study investigates a range of machine learning (ML) methods such as Logistic Regression, Support Vector Machines, and Random Forests, as well as deep learning (DL) architectures including CNNs and hybrid models like BiLSTM-CNN and CNN-GRU for extracting textual features. On the transformer side, we explored multiple pretrained models including mBERT, MuRIL, and BERT- base-chinese to capture nuanced language representations. These textual models were fused with visual features extracted from pretrained ResNet50 and DenseNet121 architectures using both early and decision-level fusion strategies. Among all evaluated configurations, the BERT-base-chinese + ResNet50 early fusion model achieved the best overall performance, with a macro F1-score of 0.8541, ranking 4th in the shared task. These findings underscore the effectiveness of combining pretrained vision and language models for tackling multimodal hate speech detection.

Hate speech on social media is an evolving problem, particularly in low-resource languages like Tamil, where traditional hate speech detection approaches remain under developed. In this work, we provide a focused solution for cast and migration-based hate speech detection using Tamil-BERT, a Tamil-specialized pre-trained transformer model. One of the key challenges in hate speech detection is the severe class imbalance in the dataset, with hate speech being the minority class. We solve this using focal loss, a loss function that gives more importance to harder-to-classify examples, improving the performance of the model in detecting minority classes. We train our model on a publicly available labeled dataset of Tamil text as hate and non-hate speech. Under strict evaluation, our approach achieves impressive results, outperforming baseline models by a considerable margin. The model achieves an F1 score of 0.8634 and good precision, recall, and accuracy, making it a robust solution for hate speech detection in Tamil. The results show that fine-tuning transformer-based models like Tamil-BERT, coupled with techniques like focal loss, can substantially improve performance in hate speech detection for low-resource languages. This work is a contribution to this growing amount of research and provides insights on how to tackle class imbalance for NLP tasks.