Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm

Abstract

With recent advancements in social media and technology as a whole, online news sources have increased. Therefore there has been a higher demand of people wanting a convenient way to find recent, relevant and updated online news articles and posts from social media platforms. In the current status quo, many people feel comfortable with their main source of news being social media articles. Unfortunately, receiving news via social media platforms and unverified online sites has aroused many problems, one of which being fake news (news which contain incorrect or biased facts and statements). Many individuals all around the world are vulnerable and subject to fake news and becoming victims of propaganda and/or being misinformed. To solve this world-wide complication, we used word preprocessing skills to digest the content of articles, and used several mathematical vectors to pinpoint the legitimacy of a news article. To establish an accurate system, words used in examples of fake news and real news were collected using Python. Verifying fake and real news is an important process that all news should go through as it can result in immense consequences. Data on real news and fake news were collected from Kaggle. We had the conclusion that the trained machine learning algorithms showed high accuracy of distinguishing which indicates our research was successful.