Comparison of Random Forest with K-Nearest Neighbors to Detect Fake News with Improved Accuracy

Aim: To develop an automated, reliable and effective system that detects fake news articles, with the goal of reducing the spread of misinformation and promoting the dissemination of accurate and trustworthy information because the rapid spread of fake innovative news has dturn out to be a foremost concern in modern years. Materials and Methods: The effectiveness of two methods Random Forest and K Nearest Neighbor (KNN) are compared in predicting fake news. The evaluation was carried out using a Github dataset of 2000 newscast informations labeled as either counterfeit or unaffected, with performance metrics such as accuracy used to compare the two algorithms. The model size of the group is 10. Results and Discussions: The result shows that KNN outperformed Random Forest (RF) in terms of all the performance metrics, suggesting that KNN is a more effective method for detecting fake news. The significance value for this study is p=0.001 which is less than 0.05. Hence, there is a statistically significant difference between the two groups. Conclusion: The results suggest that KNN with 81.20% accuracy is a more effective algorithm for fake news detection. This study provides valuable insights into the effectiveness of Machine Learning (ML) algorithms in detecting fake-news.