Publication Date

Spring 2020

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Robert Chun

Second Advisor

Thomas Austin

Third Advisor

Vinh Phuong

Keywords

​Linguistic Analysis, Deep Learning, Fake news ​Classifiers

Abstract

The spread of information about current events is a way for everybody in the world to learn and understand what is happening in the world. In essence, the news is an important and powerful tool that could be used by various groups of people to spread awareness and facts for the good of mankind. However, as information becomes easily and readily available for public access, the rise of deceptive news becomes an increasing concern. The reason is due to the fact that it will cause people to be misled and thus could affect the livelihood of themselves or others. The term that is coined for spreading false information is known as fake news. It is of the utmost importance to mitigate this issue, thus the proposition is to perform a study on technological techniques that are being used to prevent the spread of dishonest and propagandized information. Since there are an abundance of websites and articles that internet users could read, the use of automated technology was the only logical option when dealing with fake news. The techniques that were used in this study were based around linguistic analysis and deep learning. The end objective was to create a classifier that was able to judge an article based on the amount of fake news within it. Experiments were performed on these classifiers, which tried to prove that applied linguistic analysis was important in improving the accuracy of the classifiers. The results from this study displayed evidence that applied linguistic analysis did not have a sufficient impact, whereas deep learning and dataset improvements did have an impact.

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