Machine Learning and the Challenge of Predicting Fake News

Many Natural Language Processing (NLP) techniques exist for detecting "fake news". Multi-phase algorithms with Determined Decision Trees, Gradient Enlargement, and others have been used by various researchers and organizations with varying results. One study from researchers at Rensselaer Polytechnic Institute reported 83% accuracy in predicting whether a news article is from a reliable or unreliable source [1], while Facebook's 2019 attempt at developing an algorithm failed miserably, with some users experiencing a "maelstrom" of fake news [2]. A new study, published in the November 2021 issue of the Journal of Emerging Technologies and Innovative Research [3] performs an analysis of a wide range of AI models for efficacy, finding that models generally perform poorly, ranging from 60% to 77% accuracy. Separating fake news from real news is a challenge even for the most sophisticated AI. Simple content-related programs and shallow marking of the speech part (POS) fail to consider contextual information and are unable to accurately classify news stories as fact or fake unless combined with more sophisticated algorithms.