Machine Learning Framework for Detecting Fake News and Combating Misinformation Spread on Facebook Platforms

Abstract

The spread of fake news on social media platforms like Facebook threatens societal harmony and undermines the reliability of information. To address this issue, this research employs machine learning techniques to construct a robust and scalable framework for detecting fake news. Using a well-curated dataset of labeled Facebook posts containing both authentic and fake news, the study ensures a balanced representation for effective learning. Textual data was transformed into numerical features through Term Frequency-Inverse Document Frequency (TF-IDF) preprocessing, enabling seamless integration with machine learning algorithms. A variety of classification models, including Support Vector Machines (SVM), Logistic Regression, Gradient Boosting, and Random Forest, were trained and evaluated. Six performance evaluations precision, accuracy, F1 score, recall, Matthews Correlation Coefficient (MCC), and area under the Receiver Operating Characteristic (ROC) curve—were used to measure model effectiveness. The results highlighted Gradient Boosting as the most effective algorithm, achieving superior accuracy and overall performance. This framework demonstrates the capability of machine learning to automate the detection of misinformation, offering a scalable and efficient solution for preserving content credibility on Facebook. The study contributes significantly to the broader effort of combating misinformation, ensuring the dissemination of reliable information, and safeguarding public trust on social media platforms