  Fake news is a type of pervasive propaganda that spreads misinformation
online, taking advantage of social media's extensive reach to manipulate public
perception. Over the past three years, fake news has become a focal discussion
point in the media due to its impact on the 2016 U.S. presidential election.
Fake news can have severe real-world implications: in 2016, a man walked into a
pizzeria carrying a rifle because he read that Hillary Clinton was harboring
children as sex slaves. This project presents a high accuracy (87%) machine
learning classifier that determines the validity of news based on the word
distributions and specific linguistic and stylistic differences in the first
few sentences of an article. This can help readers identify the validity of an
article by looking for specific features in the opening lines aiding them in
making informed decisions. Using a dataset of 2,107 articles from 30 different
websites, this project establishes an understanding of the variations between
fake and credible news by examining the model, dataset, and features. This
classifier appears to use the differences in word distribution, levels of tone
authenticity, and frequency of adverbs, adjectives, and nouns. The
differentiation in the features of these articles can be used to improve future
classifiers. This classifier can also be further applied directly to browsers
as a Google Chrome extension or as a filter for social media outlets or news
websites to reduce the spread of misinformation.
