The Phenomenon Of Fake News Is Experiencing A Rapid And Growing Progress With The Evolution Of The Means Of Communication And Social Media. Fake News Detection Is An Emerging Research Area Which Is Gaining Big Interest. It Faces However Some Challenges Due To The Limited Resources Such As Datasets And Processing And Analyzing Techniques. In This Work, We Propose A System For Fake News Detection That Uses Machine Learning Techniques. We Used Term Frequency Inverse Document Frequency Of Bag Of Words And N-grams As Feature Extraction Technique, And Naïve Bayes As A Classifier. We Propose Also A Dataset Of Fake And True News To Train The Proposed System. Obtained Results Show The Efficiency Of The System
