M.S. (Master of Science)
Department of Computer Science
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.
Rahman, Ashiqur, "Modeling and Visualization of Long-Term Public Opinion on COVID-19 Vaccine" (2022). Graduate Research Theses & Dissertations. 7577.
Northern Illinois University
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