Date of Award


Document Type

Campus Access Thesis

Degree Name

Master of Science (MS)


Information Technology

First Advisor

Romilla Syed

Second Advisor

Atreya Chakraborty

Third Advisor

Lucia Silva-Gao


The spread of misinformation on social media in the ongoing COVID-19 pandemic is a great concern for public health. In this study, we build a misinformation dataset by web scraping six fact-checking websites. Using a topic modeling approach, we then analyze the dataset for 8210 fact-checked articles debunked from Jan-2020 to Aug-2020. Our analysis resulted in 13 categories/trends and 35 subcategories of misinformation. Next, we utilize the output of topic modeling to train a classifier that predicts the misinformation category. The evaluation results suggest that the Multiclass Support Vector Machine (MSVM)- based classifier achieved high performance for accuracy (88%), precision (85%), recall (83%), and F-measure (82%). In addition, we also explores public health beliefs related to COVID-19 misinformation using the Health Belief Model (HBM) configuration. Finally, we discuss the implications of our findings for health and regulatory agencies and social media organizations.


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