Publication Date

2022

Document Type

Dissertation/Thesis

First Advisor

Alhoori, Hamed

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Computer Science

Abstract

The coronavirus pandemic created significant dependence on social media. While the social web was crucial in spreading timely information and informing the public, misinformation has also spread with little to no oversight. Several works have focused on identifying misinformation and topic analysis in COVID-19 (SARS-COV-2) tweets. While most of the previous studies focus on a shorter time frame, we analyzed a larger dataset starting from the beginning of the pandemic until the end of December 2021. Our work focuses on a novel area that identifies the motivating and demotivating topics of COVID-19 vaccination and analyzes these topics based on time, geographic location, and political orientation. We noticed that while the motivating topics mostly stay the same over time and geographic location, the demotivating topics vary rapidly. We developed an interactive visualization to understand the information better and find hidden relations between the public stance and the topics. Finally, we expanded our study to build a model to identify possible spamming by bots sharing scholarly articles. We found that health science literature is more spammed than other scientific areas.

Extent

113 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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