Fake News Detection Using Machine Learning

The news which is specially created to misguide as well as mislead the readers is termed fake news. Fake news can cause potential harm to both the individual and society. The problem of fake news has increased far more quickly in recent years. Social networks have significantly changed the scope and effect of their overall influence. Daily, a huge amount of information is released in print and online media, but it can be difficult to determine if the information is accurate or not. A significant amount of study has been done in this area in recent years with positive outcomes. In this modern era, machine learning is the answer to every issue. It can provide answers that people cannot readily think of and is applied in practically all disciplines, domains, and research. Many different algorithms in machine learning and artificial intelligence can help us in identifying as well as eliminating fake or false news. In this article, supervised learning is used. To determine the false news, the authors have used four machine learning algorithms in this study: Decision Tree (DT) classification, Logistic Regression (LR), Random forest (RF) and Passive Aggressive classifier (PAC). After comparing the output of each method, the one with the best accuracy is selected. The PAC outperforms the other classifiers and produces the best result.