During the COVID-19 a pandemic, mathematicians mobilized to create models to predict the rise of COVID-19 through communities. In parallel to the spread of the virus, there was an equally insidious spread of misinformation across various social media platforms. In this poster, we will analyze the similarities and differences in transmission of various types of COVID-19 misinformation (e.g, conspiratorial and non-conspiratorial) via semi-viral tweets in the early stages of the pandemic. We build and analyze follower/followee network graphs for retweets of different types of misinformation and determine the characteristics that distinguish the spread conspiratorial versus non-conspiratorial misinformation.
Additional authors: Maia Powell, UC Merced; Emilio Lobato, UC Merced