ABSTRACT-The rise of online social networks has led to a surge in fake news, often used for commercial and political gain. These deceptive messages easily mislead users, causing significant offline societal impacts. Improving information trustworthiness in these networks necessitates the timely identification of fake news. This paper explores the principles, methodologies, and algorithms for detecting fake news articles, creators, and subjects on social networks and evaluates their effectiveness. The challenge lies in managing the vast amount of data to identify and correct misinformation, particularly on social media. We propose a method for detecting fake news on Facebook using a Support Vector Machine (SVM) model to predict the authenticity of posts. The paper also discusses techniques to enhance detection accuracy. Results indicate that machine learning methods can effectively address the fake news detection problem.