Computer Science ETDs

Publication Date

Summer 7-1-2018

Abstract

Social media provide communication networks for their users to easily create and share content. Automated accounts, called bots, abuse these platforms by engaging in suspicious and/or illegal activities. Bots push spam content and participate in sponsored activities to expand their audience. The prevalence of bot accounts in social media can harm the usability of these platforms, and decrease the level of trustworthiness in them. The main goal of this dissertation is to show that temporal analysis facilitates detecting bots in social media. I introduce new bot detection techniques which exploit temporal information. Since automated accounts are controlled by computer programs, the existence of patterns among their temporal behavior is highly predictable. On the other hand, patterns emerge in human temporal behavior as well since humans follow cyclic schedule. Therefore, we need a solution that can differentiate between these two classes by learning patterns of each. For my Ph.D. dissertation, I focus on the temporal behavior of social media users for the following purposes: 1. to show that high temporal correlation among users is common with automated accounts, 2. to design a system, called DeBot, which detects highly correlated accounts, 3. to improve the time complexity of calculating correlation for real-time applications, and 4. to deploy deep learning techniques on temporal information to classify social media users.

Language

English

Keywords

Social Media, Bot Detection, Time Series Mining, Data Mining

Document Type

Dissertation

Degree Name

Computer Science

Level of Degree

Doctoral

Department Name

Department of Computer Science

First Committee Member (Chair)

Abdullah Mueen

Second Committee Member

Jared Saia

Third Committee Member

Jedidiah Crandall

Fourth Committee Member

Danai Koutra

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