Comparative Analysis of Machine Learning Algorithms for Detecting Fake News: Efficacy and Accuracy in the Modern Information Ecosystem

Abstract

In an era where the spread of fake news poses a significant threat to the integrity of the information landscape, the need for effective detection tools is paramount. This study evaluates the efficacy of three machine learning algorithms—Multinomial Naive Bayes, Passive Aggressive Classifier, and Logistic Regression—in distinguishing fake news from genuine articles. Leveraging a balanced dataset, meticulously processed and vectorized through Term Frequency-Inverse Document Frequency (TF-IDF), we subjected each algorithm to a rigorous classification process. The algorithms were evaluated on metrics such as precision, recall, and F1-score, with the Passive Aggressive Classifier outperforming others, achieving a remarkable 0.99 in both precision and recall. Logistic Regression followed with an accuracy of 0.98, while Multinomial Naive Bayes displayed robust recall at 1.00 but lower precision at 0.91, resulting in an accuracy of 0.95. These metrics underscored the nuanced capabilities of each algorithm in correctly identifying fake and real news, with the Passive Aggressive Classifier demonstrating superior balance in performance. The study's findings highlight the potential of employing machine learning techniques in the fight against fake news, with the Passive Aggressive Classifier showing promise due to its high accuracy and balanced precision-recall trade-off. These insights contribute to the ongoing efforts in digital media to develop advanced, ethical, and accurate tools for maintaining information veracity. Future research should continue to refine these models, ensuring their applicability in diverse and evolving news ecosystems.