The advent and expansion of social media have transformed global communication. Despite its numerous advantages, it has also created an avenue for the rapid spread of fake news, which can impact people’s decision-making and judgment. This study explores detecting fake news as part of the DravidianLangTech@NAACL 2025 shared task, focusing on two key tasks. The aim of Task 1 is to classify Malayalam social media posts as either original or fake, and Task 2 categorizes Malayalam-language news articles into five levels of truthfulness: False, Half True, Mostly False, Partly False, and Mostly True. We accomplished the tasks using transformer models, e.g., M-BERT and classifiers like Naive Bayes. Our results were promising, with M-BERT achieving the better results. We achieved a macro-F1 score of 0.83 for distinguishing between fake and original content in Task 1 and a score of 0.54 for classifying news articles in Task 2, ranking us 11 and 4, respectively.

Social media in the present world is an essential communication platform for information sharing. But their emergence has now led to an increase in the proportion of online abuse, in particular against women in the form of abusive and offensive messages. A reflection of the social inequalities, the importance of detecting abusive language is highlighted by the fact that the usage has a profound psychological and social impact on the victims. This work by DravidianLangTech@NAACL 2025 aims at developing an automated abusive content detection system for women directed towards women on the Tamil and Malayalam platforms, two of the Dravidian languages. Based on a dataset of their YouTube comments about sensitive issues, the study uses multilingual BERT (mBERT) to detect abusive comments versus non-abusive ones. We achieved F1 scores of 0.75 in Tamil and 0.68 in Malayalam, placing us 13 and 9 respectively.

The rise of AI-generated content has introduced challenges in distinguishing machine-generated text from human-written text, particularly in low-resource languages. The identification of artificial intelligence (AI)-based reviews is of significant importance to preserve trust and authenticity on online platforms. The Shared Task on Detecting AI-Generated Product Reviews in Dravidian languages deals with the task of detecting AI-generated and human-written reviews in Tamil and Malayalam. To solve this problem, we specifically fine-tuned mBERT for binary classification. Our system achieved 10th place in Tamil with a macro F1-score of 0.90 and 28th place in Malayalam with a macro F1-score of 0.68, as reported by the NAACL 2025 organizers. The findings demonstrate the complexity involved in the separation of AI-derived text from human-authored writing, with a call for continued advances in detection methods.