Predicting Changes In Public Opinion With Twitter: What Social Media Data Can And Can't Tell Us About Opinion Formation

With the advent of social media data, some researchers have claimed it has the potential to revolutionize the measurement of public opinion. Others have pointed to non-generalizable methods and other concerns to suggest that the role of social media data in the field is limited. Likewise, researchers remain split as to whether automated social media accounts, or bots, have the ability to influence conversations larger than those with their direct audiences. This dissertation examines the relationship between public opinion as measured by random sample surveys, Twitter sentiment, and Twitter bot activity. Analyzing Twitter data on two topics, the president and the economy, as well as daily public polling data, this dissertation offers evidence that changes in Twitter sentiment of the president predict changes in public approval of the president fourteen days later. Likewise, it shows that changes in Twitter bot sentiment of both the president and the economy predict changes in overall Twitter sentiment on those topics between one and two days later. The methods also reveal a previously undiscovered phenomenon by which Twitter sentiment on a topic moves counter to polling approval of the topic at a seven-day interval. This dissertation also discusses the theoretical implications of various methods of calculating social media sentiment. Most importantly, its methods were pre-registered so as to maximize the generalizability of its findings and avoid data cherry-picking or overfitting.