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
Neil Christian R. Riego Danny Bell Villarba
The digitalization of news media become a good indicator of progress and
signal to more threats. Media disinformation or fake news is one of these
threats, and it is necessary to take any action in fighting disinformation.
This paper utilizes ground truth-based annotations and TF-IDF as feature
extraction for the news articles which is then used as a training data set for
Multinomial Naive Bayes. The model has an accuracy of 99.46% in training and
88.98% in predicting unseen data. Tagging fake news as real news is a
concerning 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 suggested
to further improve the corpus collection, and use an ensemble machine learning
to reinforce the prediction