Detection of Fake News Based on Typical Machine Learning Models

Abstract

With the rapid expansion of the network, the glut of news spread everywhere. Because of the obscurity of news sources and the unrestricted types of viewers, the harmful impact of false news is more pervasive than ever before. The goal of this study is to evaluate the efficacy of five machine learning models, namely Decision Tree, Logistic Regression, Random Forest, Multilayer Perceptron (MLP) and Naive Bayes to detect false news using a dataset obtained from Kaggle. Following the application of five models for predicting false news based on the news' title and comparison of the training and testing accuracies of each model, the results indicate that Random Forest is the best model, with Decision Tree and MLP models also having very high testing accuracies. Surprisingly, the Naive Bayes model, widely recognized as the optimal classifier for text data, had the lowest testing accuracy in this study, implying that more research is required to explain this outcome. Finally, the limits of current machine learning algorithms, as well as the possibility of bias in datasets, provide a good direction for future studies.