Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News

There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't what makes those fake news spread. The threat grows, and some point, it gets out hand. That's why this paper is trying to figure how predict the features make It's using Naive Bayes, Logistic Regression, Random forest Machine Learning do this. Online stories were made up used. They found by Term Frequency-Inverse Document Frequency (TF-IDF). best partition for testing validating prediction was chosen random used in analysis. So, all three machine learning classifications predicting done. results experiment show Forest Classifier outperformed other two algorithms. It had TF-IDF with accuracy 86 percent. Bayes rate 84%, Regression 85%, so did well. As such, model shows most essential point about content news.