推特愤怒预测2016年美国总统大选的县级结果

IF 2 4区 心理学 Q3 PSYCHOLOGY, SOCIAL
K. Bernecker, Michael Wenzler, K. Sassenberg
{"title":"推特愤怒预测2016年美国总统大选的县级结果","authors":"K. Bernecker, Michael Wenzler, K. Sassenberg","doi":"10.5334/IRSP.256","DOIUrl":null,"url":null,"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.","PeriodicalId":45461,"journal":{"name":"International Review of Social Psychology","volume":" ","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2019-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Tweeted Anger Predicts County-Level Results of the 2016 United States Presidential Election\",\"authors\":\"K. Bernecker, Michael Wenzler, K. Sassenberg\",\"doi\":\"10.5334/IRSP.256\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":45461,\"journal\":{\"name\":\"International Review of Social Psychology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2019-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Review of Social Psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.5334/IRSP.256\",\"RegionNum\":4,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PSYCHOLOGY, SOCIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Review of Social Psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.5334/IRSP.256","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PSYCHOLOGY, SOCIAL","Score":null,"Total":0}
引用次数: 6

摘要

2016年美国总统大选结束后,专家和记者猜测,愤怒的选民支持了出乎意料的赢家唐纳德·特朗普。目前的研究使用了来自1347个县的美国公民发布的1.48亿条推文样本,根据情感内容进行分类,以预测县一级的选举结果。正如预期的那样,特朗普在推特上愤怒和负面情绪更多的县获得了更多的支持,即使控制了之前总统选举中的各种县特征和保守投票选择。这些发现可能是情绪共鸣的结果——选民会被符合他们情绪的政治诉求所吸引——因为在2012年和2016年的竞选中,特朗普比其他总统候选人使用了更多的愤怒和负面情绪词汇。研究结果表明,负面情绪在2016年总统大选中发挥了关键作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.20
自引率
8.00%
发文量
7
审稿时长
16 weeks
期刊介绍: The International Review of Social Psychology (IRSP) is supported by the Association pour la Diffusion de la Recherche Internationale en Psychologie Sociale (A.D.R.I.P.S.). The International Review of Social Psychology publishes empirical research and theoretical notes in all areas of social psychology. Articles are written preferably in English but can also be written in French. The journal was created to reflect research advances in a field where theoretical and fundamental questions inevitably convey social significance and implications. It emphasizes scientific quality of its publications in every area of social psychology. Any kind of research can be considered, as long as the results significantly enhance the understanding of a general social psychological phenomenon and the methodology is appropriate.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信