Detecting Fake News Using Machine Learning: A Comparative Analysis Of Logistic Regression, Random Forest, And Gradient Boosting

The rapid spread of misinformation in the digital era has emerged as a serious concern, shaping public opinion and disrupting societal stability.This research focuses on the study evaluates models including Logistic Regression, Random Forest, and Gradient Boosting, with Gradient Boosting achieving the highest performance.A well-structured dataset, containing both verified real and fabricated news articles, was utilized for training and evaluating the system's effectiveness.Text preprocessing steps, including tokenization, stemming, and vectorization, were implemented to convert raw text into a structured format suitable for analysis.Various classification algorithms, such as Logistic Regression, Gradient Boosting, and Random Forest, were assessed to determine the most efficient model for distinguishing false information from credible sources.The proposed approach demonstrated high accuracy in identifying deceptive content, reinforcing the importance of computational techniques in mitigating misinformation.The findings highlight the significance of automated detection systems in reducing the spread of unreliable news, contributing to a more informed and responsible digital environment.