Examining production, dissemination, and consumption of misinformation: the case of COVID-19 pandemic

Nowadays, social media is a crucial part of our lives. Platforms like Facebook and Twitter play indispensable roles in the modern information ecosystem, impacting many areas of society. Prevalence of users’ speculation and mistrust makes social media a hotbed of misinformation, which is information that is wrong or misleading. Misinformation is one of the biggest concerns associated with the use of social media platforms. The COVID-19 pandemic has become a hot topic of misinformation. Huge amounts of misinformation related to the pandemic have been created on social media, covering the public issues such as facial masks, the COVID test and vaccines, and lockdown policies. One of the consequences of misinformation is opinion polarization, a state in which people are divided into camps such that opinions of people in the same camp are homogenous, while opinions across camps become heterogeneous, even opposite. Social media users with polarized opinions are prone to believing in and spreading misinformation. The lifecycle of misinformation on social media involves three main components: the root messages which contain misinformation, the producers who produce the root messages, and the consumers who consume the root messages and help spread them further. In this dissertation, I studied these three components’ roles in production, dissemination, consumption, and mitigation of misinformation with a focus on the producers of misinformation. Three interrelated research essays have been conducted based on a large, original data set of COVID-19-related misinformation on Twitter. Essay I explores the question: how do producers, root messages, and consumers interact in the production and diffusion of misinformation on social media, and what roles does each of them play? Essay II further anchors on the producers and asks: can producers’ communicative intentions, their choice of semantic-linguistic methods, and their polarity of opinion influence the diffusion of misinformation? Finally, Essay III asks: how to reduce misinformation’s diffusion by leveraging the knowledge of the producers, consumers, and root messages obtained in Essays I and II using the predictive modeling technology? These essays mainly address the research gap that little research has been focused on the roles of misinformation producers in misinformation diffusion. The research can generate deeper understanding of the mechanism behind misinformation diffusion