Accuracy and Execution Time in Fake News Detection: A Comparative Analysis of Logistic Regression and Naïve Bayes

The emergence of online misinformation has produced fake news detection as a significant field of study. Machine learning algorithms like Logistic Regression and Naïve Bayes are commonly used to detect fake news because they are efficient and interpretable. This research compares the performance of the two algorithms in identifying fake news based on accuracy, precision, recall, F1-score, and the time taken to execute. While precision is vital in classification, execution time becomes important in real-time scenarios. The research compares the models with respect to handling large data sets and classifying news articles successfully. Furthermore, the computational cost of each algorithm is compared to establish its usability for large-scale applications. The results indicate fundamental trade-offs between speed and precision, stressing the importance of optimal models in preventing misinformation. Future work may consider hybrid methods or deep learning methods to enhance rates of detection while ensuring computational efficiency