Enhanced Content-Based Fake News Detection Methods with Context-Labeled News Sources

Abstract

This work examined the relative effectiveness of multilayer perceptron, random forest, and multinomial naïve Bayes classifiers, trained using bag of words and term frequency-inverse dense frequency transformations of documents in the Fake News Corpus and Fake and Real News Dataset. The goal of this work was to help meet the formidable challenges posed by proliferation of fake news to society, including the erosion of public trust, disruption of social harmony, and endangerment of lives. This training included the use of context-categorized fake news in an effort to enhance the tools’ effectiveness. It was found that term frequency-inverse dense frequency provided more accurate results than bag of words across all evaluation metrics for identifying fake news instances, and that the Fake News Corpus provided much higher result metrics than the Fake and Real News Dataset. In comparison to state-of-the-art methods the models performed as expected