Support for populism has grown substantially during the past 2 decades, a development that has coincided with a marked increase in the experience of negative affect around the world. We use a multimodal, multimethod empirical approach, with data from a diverse set of geographical and political contexts, to investigate the extent to which the rising electoral demand for populism can be explained by negative affect. We demonstrate that negative affect-measured via (a) self-reported emotions in surveys as well as (b) automated text analyses of Twitter data-predicts individual-level populist attitudes in two global surveys (Studies 1a and 1b), longitudinal changes in populist party vote shares at general elections in Europe (Study 2), district-level Brexit voting in the 2016 U.K. referendum (Study 3), and county-level vote shares for Donald Trump in the 2016 and 2020 U.S. presidential elections (Studies 4a and 4b). We find that negative emotions-such as fear and anger as well as more often overlooked low-arousal negative emotions like depression and sadness-are predictive of populist beliefs as well as voting and election results at scale. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

---

负面影响在塑造民粹主义支持方面的作用：来自全球各地的现场证据的汇聚。


过去 20 年来，对民粹主义的支持大幅增长，这一发展与世界各地负面影响的显着增加同时发生。我们采用多模式、多方法的实证方法，利用来自不同地理和政治背景的数据，来调查民粹主义选举需求上升在多大程度上可以用负面影响来解释。我们证明，在两项全球调查（研究 1a 和 1b）中，通过 (a) 调查中自我报告的情绪以及 (b) Twitter 数据的自动文本分析来衡量的负面情绪可以预测个人层面的民粹主义态度，欧洲大选中的民粹主义政党投票份额（研究 2）、2016 年英国公投中的区级脱欧投票（研究 3）以及 2016 年和 2020 年美国总统选举中唐纳德·特朗普的县级投票份额（研究 4a 和4b）。我们发现，负面情绪（例如恐惧和愤怒）以及更常被忽视的低唤醒负面情绪（例如抑郁和悲伤）可以预测民粹主义信仰以及大规模的投票和选举结果。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。