Enhancing Media Integrity: Leveraging Machine Learning for Accurate Detection of Fake News and Misleading Information

Discovering fake news is very important in the press to maintain credibility and combat wrong information.Traditional fake news detection systems (FNDS) are challenged due to accuracy problems due to the complex nature of misinformation in news articles.This study introduces a new approach using a group method that combines automatic learning algorithms such as naive Bays, K-NN, Random Forest, Decision Tree, and Support Vector Machine (SVM) by integrating majority votes.The results indicate that group technologies greatly enhance the accuracy of defining the fake news compared to the algorithm-based works.By leveraging collective intelligence, FNDS addresses the complexities of fake news detection.This approach enhances its capability to identify and refute misinformation effectively.This research emphasizes the choice of appropriate algorithms and the integration of the group's methods to develop the most accurate and strong fake news discovery systems, contributing to combating wrong information in the press.The proposed approach achieved an accuracy improvement of 15% compared to individual algorithms, with an overall F1-score of 89.5%.