Detecting Fake News Using Machine Learning

Abstract

Fake news has had a significant effect on society and politics. To aid in combating the spread of misinformation, we worked to develop a machine learning algorithm that could detect fake news based on textual data. We used a count vectorizer to vectorize our text which we then inputted into Logistic Regression, Support Vector Machine (SVM), and Linear Support Vector Classifier (SVC) models. The greatest accuracy score achieved was 99.97% with the Linear SVC. We discovered however that there was a significant difference in how the real and fake news datasets were constructed that would not translate into real life: the true news articles contained quotation marks, apostrophes, and dashes while these characters were not present in the fake news articles. Because of this, we also developed a more applicable Logistic Regression model removing these specific characters from the dataset all together with an accuracy score of 98.4%.