On January 5, 2020, the World Health Organization (WHO) reported on the outbreak of pneumonia of unknown cause in Wuhan, China. Two weeks later, a 35-year-old Washington resident checked into a local urgent care clinic with a 4-day cough and fever. Laboratory testing would confirm this individual as the first case of the novel coronavirus in the U.S., and on January 20, 2020, the Center for Disease Control (CDC) reported this case to the public. In the days and weeks to follow, Twitter, a social media platform with 450 million active monthly users as of 2020, provided many American residents the opportunity to share their thoughts on the developing pandemic online. Social media sites like Twitter are a prominent source of discourse surrounding contemporary political issues, allowing for direct communication between users in real-time. As more population centers around the world gain access to the internet, most democratic discussion, both nationally and internationally, will take place in online spaces. The activity of elected officials as private citizens in these online spaces is often overlooked. I find the ability of publics—which philosopher John Dewey defines as groups of people with shared needs—to communicate effectively and monitor the interests of political elites online to be lacking. To best align the interests of officials and citizens, and achieve transparency between publics and elected officials, we need an efficient way to measure and record these interests. Through this thesis, I found that natural language processing methods like sentiment analyses can provide an effective means of gauging the attitudes of politicians towards contemporary issues.