Abstract
In the aftermath of the 2016 United States presidential election, experts and journalists speculated that angry voters had supported the unexpected winner Donald Trump. The present study used a sample of 148 million tweets posted by U.S. citizens from across 1,347 counties, classified with regard to emotional content, to predict the election results at county level. As expected, Donald Trump received more support in counties where people tweeted more anger and negative emotions, even when various county characteristics and conservative vote choice in the preceding presidential election were controlled. These findings might be an outcome of emotional resonance—voters being attracted by political appeals that match their emotions—because Trump used more anger and negative emotion words in his campaign than the other presidential candidates in 2012 and 2016. The findings suggest that negative emotions played a critical role in the 2016 presidential election.