Tweeted Anger Predicts County-Level Results of the 2016 United States Presidential Election
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ubiquity Press
2019-05-01
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| Series: | International Review of Social Psychology |
| Subjects: | |
| Online Access: | https://www.rips-irsp.com/articles/256 |
| _version_ | 1818507343024881664 |
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| author | Katharina Bernecker Michael Wenzler Kai Sassenberg |
| author_facet | Katharina Bernecker Michael Wenzler Kai Sassenberg |
| author_sort | Katharina Bernecker |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T22:17:10Z |
| format | Article |
| id | doaj.art-08492309e7ad4e7d9707369b7818b454 |
| institution | Directory Open Access Journal |
| issn | 2397-8570 |
| language | English |
| last_indexed | 2024-12-10T22:17:10Z |
| publishDate | 2019-05-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | International Review of Social Psychology |
| spelling | doaj.art-08492309e7ad4e7d9707369b7818b4542022-12-22T01:31:26ZengUbiquity PressInternational Review of Social Psychology2397-85702019-05-0132110.5334/irsp.25678Tweeted Anger Predicts County-Level Results of the 2016 United States Presidential ElectionKatharina Bernecker0Michael Wenzler1Kai Sassenberg2Leibniz-Institut für Wissensmedien (IWM)Leibniz-Institut für Wissensmedien (IWM)Leibniz-Institut für Wissensmedien (IWM)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.https://www.rips-irsp.com/articles/256political preferenceemotional resonancenegative emotionsanger |
| spellingShingle | Katharina Bernecker Michael Wenzler Kai Sassenberg Tweeted Anger Predicts County-Level Results of the 2016 United States Presidential Election International Review of Social Psychology political preference emotional resonance negative emotions anger |
| title | Tweeted Anger Predicts County-Level Results of the 2016 United States Presidential Election |
| title_full | Tweeted Anger Predicts County-Level Results of the 2016 United States Presidential Election |
| title_fullStr | Tweeted Anger Predicts County-Level Results of the 2016 United States Presidential Election |
| title_full_unstemmed | Tweeted Anger Predicts County-Level Results of the 2016 United States Presidential Election |
| title_short | Tweeted Anger Predicts County-Level Results of the 2016 United States Presidential Election |
| title_sort | tweeted anger predicts county level results of the 2016 united states presidential election |
| topic | political preference emotional resonance negative emotions anger |
| url | https://www.rips-irsp.com/articles/256 |
| work_keys_str_mv | AT katharinabernecker tweetedangerpredictscountylevelresultsofthe2016unitedstatespresidentialelection AT michaelwenzler tweetedangerpredictscountylevelresultsofthe2016unitedstatespresidentialelection AT kaisassenberg tweetedangerpredictscountylevelresultsofthe2016unitedstatespresidentialelection |