Exploratory data analysis and deception detection in news articles on social media using machine learning classifiers

Abstract

This paper investigates realistic ways to identify fake news on digital platforms in this context automatically. To begin, a massive number of current and correlated works were surveyed in an attempt to incorporate all possible features for detecting fake news, followed by exploratory data analysis to identify sources that frequently publish fake news and determine the most frequently occurring words in the title and body of fake and genuine news. Our findings indicate that the suggested computer models possess an advantageous discriminative potential for detecting fake news transmitted via digital channels. In this paper, we classify documents into fake/real news categories using Random Forest (RF), Naive Bayes (NB), and Passive Aggressive (PA)] machine learning classifiers with and without text processing (TP). Our paper's result is determined and calculated using the confusion matrix and the classifier’s performance by defining accuracy, precision, recall, and F1 score metrics for fake news detection.

Keywords

• Fake news
• Fake news detection
• Machine learning classifiers
• Random Forest
• Naïve Bayes
• Passive aggressive