Features Extraction Based on Probability Weighting for Fake News Classification on Social Media

Fake news is a massive problem globally, especially on social media. Most people spend a lot of time consuming social media every day, and it is very possible for people as social media users to receive fake news without realizing it. Primarily due to this situation, we developed a machine learning tool to detect fake news that operates with the aid of various algorithms such as Decision Tree, K-Nearest Neighbor, and Naïve Bayes. Our experiement is tested based on machine learning that selected only one technique used to classify the data by finding the model set. In addition, the performance of the set describes the classification of the model and the inconsistency solution for each iteration. This study proposed a model which used the probability weighting of the model in features extraction processing for data classification. The concept is the enhancement of probability weighting features that converge exactly the class labels of classification. Our work was also implemented based on traditional Count Vectorizer and TF-IDF Vectorizer sentiment analysis and combined probability weighting features for fake news articles. The experimental results of the work illustrate that the best accuracy achieved by a proposed model used probability weighting features to find out the impact of classifiers models. In addition, the results of experimental information is represented by enhancing the overall performance of Decision Tree, K-Nearest Neighbor, and Naïve Bayes with various datasets. In addition, the measures of precision, recall, F1-measure, AUC, and accuracy for each class and deep in each class were achieved and reached the highest performance of the proposed model.