Applying Sentiment Analysis to Detect Fake News in Online Content

Abstract

Fake news is spreading increasingly across social media and online platforms in a troubling manner, posing a crucial and concerning challenge for our so-ciety. Detecting misleading content and analyzing the sentiments it conveys remains a formidable challenge. This article focuses on the use of different sentiment analysis techniques to detect fake news, specifically by using ma-chine-learning algorithms to identify fake news in texts with the goal of dis-tinguishing truth from falsehood. By combining polarity analysis with ma-chine learning techniques, we observe a marked improvement in detecting deceptive content. The results obtained across various datasets show that sentiment analysis has significantly enhanced detection scores, achieving an accuracy of 99.8%.