Fake News Detection in Low Resource Language Using Machine Learning Techniques and SMOTE

Fake content dissemination is a significant challenge in the era of digital information. This paper discusses the critical issues in detecting fake content in news articles of low-resource languages, specifically focusing on the Tamil language, where the availability of labelled data and advanced natural language processing tools are limited. We employ traditional machine learning models to mitigate this problem, with a particular emphasis on the detection and classification of fake and real content in the context of Tamil news. Our study explores the performance of different models like logistic regression, support vector machines (SVM), naive Bayes, k-nearest neighbours (KNN), decision trees, random forests and passive-aggressive classifiers. By conducting a comprehensive comparative analysis of these models within the challenging linguistic environment of Tamil, we aim to provide insights into their suitability for detecting fake content in low-resource languages and draw meaningful comparisons between their performance.