Fake News Detection Approaches Based on Knowledge Style and Propagation Perspective Using Machine Learning Algorithm

Abstract

Fake news is now viewed as one of the greatest threats to democracy, journalism, and freedom of expression. In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by this online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. It has weakened public trust in governments and its potential impact on the contentious referendum and the equally divisive in election. These studies focus on fake news from four perspective: (1) the false knowledge it carries, (2) it is writing style, (3) its propagation patterns, and (4) the credibility of its creators and spreaders. We characterize each perspective with various analysable and utilizable information provided by news and its spreaders, various strategies and frameworks that are adaptable, and techniques that are applicable. The algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Naïve Bayes, and Logistic regression Classifiers to identify the fake news from real ones in each dataset and also have increased the efficiency of these algorithms by pre-processing the data to handle the imbalanced data more appropriately. Additionally, comparison of the working of these classifiers is presented along with the results. By reviewing the characteristics of fake news and open issues in fake news studies. The model proposed has achieved an accuracy of 89.98% for KNN, 90.46% for Logistic Regression, 86.89% for Naïve Bayes, 73.33% for Decision Tree and 89.33% for SVM in our experiment.