Document Type
Thesis
Date of Award
Spring 5-31-2019
Degree Name
Master of Science in Computer Science - (M.S.)
Department
Computer Science
First Advisor
James Geller
Second Advisor
Soon Ae Chun
Third Advisor
Senjuti Basu Roy
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.
Recommended Citation
Kannan Neten Dharan, Kannan Neten Dharan, "A comparative study of russian trolls using several machine learning models on twitter data" (2019). Theses. 1663.
https://digitalcommons.njit.edu/theses/1663
