Character  &  Context

Emotions, Politics, and the 2016 US Election

Illustrations of various emotions

The 2016 US presidential election brought polarized reactions, both joy and despair, to the forefront. During “Emotions & Politics: The Inextricable Link,” Allison Troy of Franklin and Marshall College and Brett Ford of the University of Toronto discussed people’s predictions about, and reactions to, the strong emotions that resulted from the election of Donald Trump.

Research about affective forecasting, or the prediction of one’s emotional future, has demonstrated repeatedly that people tend to overestimate how intense their emotional reactions to future events will be. This phenomenon, known as the impact bias, is found for both positive and negative emotions. Some past research has found that during elections, this pattern of overestimation is less clear: studies conducted during the 2004 and 2008 presidential elections found that supporters of the losing candidate overestimated their distress, while the winner’s supporters were accurate, or even underestimated their pleasure.

Allison Troy and colleagues wondered if this divergence from past literature might be caused by the group-based, partisan identities activated during a political campaign. They asked Pennsylvanians about their anticipated emotional reaction to a Trump victory before the election, as well as their degree of liking for the candidates, and followed up after the election, recording participants’ actual emotional reaction.

They found an expectancy for extreme positive or negative emotions, with few participants falling into the middle. Furthermore, a majority made accurate predictions, with 50.2% of participants predicting their exact reaction. Finally, although supporters of the losing candidate showed the impact bias, while supporters of the victor did not, degree of partisanship (Democrat vs. Republican) did not play a large role. However, while Clinton supporters, on average, fell victim to the impact bias, those who were most favourable towards her personally, and who most strongly identified as liberals, were less likely to show an impact bias, and were more accurate when predicting their negative emotions.

Given these strong emotional reactions, how might supporters of a losing candidate cope? Many people seek to avoid negative emotions, and may use emotion reappraisal techniques to reduce their intensity. Brett Ford suggests that there may be unintended consequences in doing so: namely, decreasing engagement in political action.

In a series of studies, Ford and colleagues examined how, in the wake of the 2016 election, Clinton supporters used emotional reappraisal techniques to reframe intense negative emotions. They found that those supporters who used reappraisal when responding to political distress reported significantly lower levels of negative emotions. A decrease in negative emotions, in turn, was related to a decrease in intentions to engage in political action, as well as lower levels of reported engagement after the fact.

This effect was not limited to self-driven reappraisal strategies. When Clinton supporters were asked to reappraise their emotions, using messages provided by other supporters, they too reported significantly lower levels of negative emotions. This, too, was also followed by lower political action intentions. Thus, while emotional reappraisal is an effective way to regulate negative emotions, it appears that it may have unanticipated consequences for taking action.

Written by: Sarah L. Williams, Wilfrid Laurier University, Waterloo, Ontario, Canada
"Did Voters Accurately Predict Their Emotional Responses to Trump? Affective Forecasts of the 2016 U.S Presidential Election" and "Using Reappraisal to Regulate Negative Emotion after the 2016 U.S. Presidential Election: Does Individual Emotion Regulation Trump Collective Political Action?" part of Emotions & Politics: The Inextricable Link symposium held Friday, March 2, 2018.
Talk 1: Allison Troy, Franklin & Marshall College
Talk 2: Brett Ford, University of Toronto



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