Fake News Classification using Machine Learning Techniques

Fake news exerts a pervasive and urgent influence, causing mental harm to readers. Differentiating between fake and genuine news is increasingly tricky, impacting countless lives. This proliferation of falsehoods spreads harm and misinformation and erodes trust in global information sources, affecting individuals, organizations, and nations. It requires immediate attention. To address this issue, we conducted a comprehensive study utilizing advanced techniques such as TF-IDF and feature engineering to detect fake news. WWe proposed Machine Learning Techniques (MLT), including Naïve Bayes (NB), Decision trees (DT), Support Vector Machines (SVM), Random Forest (RF), and Logistic Regression (LR) to classify news articles. Our studies involved analyzing word patterns from diverse news sources to identify unreliable news. We calculated the likelihood of an article being fake or genuine based on the extracted features and evaluated algorithm accuracy using a carefully crafted training dataset. The analysis revealed that the decision tree algorithm exhibited the highest accuracy, detecting fake news with an impressive 99.68% rate. While the remaining algorithms performed well, none surpassed the accuracy of the decision tree. TThis study highlights the immense potential of machine learning techniques in combating the pervasive menace of leaks. Our research presents a reliable and efficient method to identify and classify unreliable information, Safeguarding the integrity of news sources and protecting individuals and societies from the harmful effects of misinformation.