Date of Award

8-1-2018

Degree Name

Master of Science

Department

Computer Science

First Advisor

Rekabdar, Banafsheh

Abstract

Widespread use of social bots becomes an important issue in the social media policy making. Automatic users are used to promote political ideas, advertise, and derail the public discourse. Identifying the bots have become an increasingly difficult task due to sophistication of the tools used to run them. In this paper I explore the domain of social bot detection. The difficulty of bot classification is well studied (Kudugunta and Ferrara (2018a), Cresci, Pietro, Petrocchi, Spognardi, and Tesconi (2017)) and arises due to high dimensionality of the data and unbalanceness of the classes. In this paper, we attempt to improve the algorithm used to detect the bots by exploiting character based GRU infrastructure. We train our model on the labeled data consisted of 8 million of human- and bot-generated tweets. For a reference, we are using several other classifiers as a benchmark to estimate the performance of the model.

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