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.

Fake news detection in Malayalam is difficult due to limited data and language challenges. This study compares machine learning, deep learning, and transformer models for classification. The dataset is balanced and divided into training, development and test sets. Machine learning models (SVM, Random Forest, Naive Bayes) used TF-IDF features and deep learning models (LSTM, BiLSTM, CNN) worked with tokenized sequences. We fine-tuned transformer models like IndicBERT, MuRIL, mBERT, and Malayalam-Bert. Among them, the Malayalam-Bert model performed the best and achieved an F1 score of 86%. On the other hand mBERT performed best at spotting fake news. However, the models struggled with mixed-language text and complex writing. Despite these challenges, transformer models turned out to be the most effective for detecting fake news in Malayalam.