Date of Award

Spring 2019

Document Type

Thesis

Degree Name

Master of Science in Computer Science - (M.S.)

Department

Computer Science

First Advisor

Geller, James

Second Advisor

Chun, Soon Ae

Third Advisor

Basu Roy, Senjuti

Abstract

Ever since Russian trolls have been brought into light, their interference in the 2016 US Presidential elections has been monitored and studied thoroughly. These Russian trolls have fake accounts registered on several major social media sites to influence public opinions. Our work involves trying to discover patterns in these tweets and classifying them by using different machine learning approaches such as Support Vector Machines, Word2vec and neural network models, and then creating a benchmark to compare all the different models. Two machine learning models are developed for this purpose. The first one is used to classify any given specific tweet as either troll or non-troll tweet. The second model classifies specific tweets as coming from left trolls or right trolls, based on apparent extreme political orientation. Several kinds of statistical analysis on these tweets are performed based on the tweets and their classifications. Further, an analysis of the machine learning algorithms, using several performance criteria, is presented.

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