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
This addresses media disinformation in the Philippines, but it is incremental as it uses standard methods on new data.
The paper tackled the problem of detecting fake news in Philippine tweets by applying a Multinomial Naive Bayes model with TF-IDF features, achieving 88.98% accuracy on unseen data but with an F1 score of 89.68% indicating issues in misclassifying fake news as real.
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