Fake News Detection Using Machine Learning Algorithms

Abstract

In the era of information proliferation, the rise of fake news poses a significant threat to the integrity of digital content. This study uses machine learning algorithms[5] to address the important problem of detecting fake news. Leveraging a dataset comprising both genuine and fabricated news articles, we employ text processing techniques, including TF-IDF vectorization, to transform textual data into a machine-learning model-friendly format. Our study explores the effectiveness of Classifiers such as logistic regression, decision trees, gradient boosting, and random forest in discerning between real and fake news. A carefully selected dataset is used to train the models, and metrics like accuracy, precision, recall, and F1-score are used to thoroughly assess each model's performance. The models' levels of success vary, according to the results, and each has advantages and disadvantages. The study opens the door for further developments in this important field by providing a comparative analysis of machine learning models and insightful information about the state of fake news detection.