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The digitalization of news media become a good indicator of progress andsignal to more threats. Media disinformation or fake news is one of thesethreats, and it is necessary to take any action in fighting disinformation.This paper utilizes ground truth-based annotations and TF-IDF as featureextraction for the news articles which is then used as a training data set forMultinomial Naive Bayes. The model has an accuracy of 99.46% in training and88.98% in predicting unseen data. Tagging fake news as real news is aconcerning point on the prediction that is indicated in the F1 score of 89.68%.This could lead to a negative impact. To prevent this to happen it is suggestedto further improve the corpus collection, and use an ensemble machine learningto reinforce the prediction

Utilization of Multinomial Naive Bayes Algorithm and Term Frequency  Inverse Document Frequency (TF-IDF Vectorizer) in Checking the Credibility of  News Tweet in the Philippines