This thesis explores the COVID-19 infodemic in the United States. The Coronavirus has severely impacted the US, and misinformation is a potential contributing factor making it an exciting area of research. The study is executed by analysing search query logs from the Microsoft Bing search dataset for Coronavirus intent (2020). The main problem statement concerns misinformation search behaviour from January to August 2020. The study explores overall distributions of misinformation, including state distributions, popular misinformation types, and potential impacting factors. Various methods are used throughout the study, including manual content-coding, supervised machine learning for automatic text classification, and exploratory analysis. Findings are validated by using data from Google Trends. The top five misinformation searches were “Qanon”, “herd immunity”, “hydroxychloroquine coronavirus”, “Bill Gates coronavirus”, and “malaria drugs for coronavirus”. Initially, people were mostly searching for misinformation related to the origin of the virus. Later this changed to include miracle-cures, alternative treatments, and conspiracy theories. Wyoming was observed as having a significantly higher misinformation level both relative to total queries from the state and population size. A trend was observed of misinformation moving from the states with the largest US cities towards rural states in recent months. No association was found between state-level implemented COVID-19 policies, political orientation and misinformation levels. This study has provided insight into the changes in misinformation during COVID-19. It is suggested that further research continues the work by researching causal connections, misinformation types through topic modelling and unsupervised learning, and utilizing a similar approach to investigate the infodemic in other countries.