From associations to sarcasm: Mining the shift of opinions regarding the Supreme Court on twitter

Abstract

Twitter is a valuable source for learning about public opinion and political communication. Applying data mining to Twitter content (i.e., “Twitter mining”) offers a way to analyze large numbers of tweets to help us understand political associations the public makes. However, the use of incivility and sarcasm in political discourse may pose a challenge for Twitter mining in the context of politics. In this study, we apply Twitter mining to the 2018 confirmation of Judge Brett Kavanaugh to the Supreme Court to look for possible changes in public opinion of the Court in the wake of the confirmation hearing and to determine whether sarcasm in political messages on Twitter can alter the results of computational methods when using large datasets. Examining two waves of tweets, one in the days immediately following the confirmation and one a month later, we find evidence of a shift in public opinion as associations between the Supreme Court and partisanship emerge only in the latter period. Using sentiment analysis, we also demonstrate that sarcasm led to over-categorization of positive tweets, which altered the results by suggesting that the public viewed partisanship on the Supreme Court favorably.