Arabaghatta Basavaraj Kiran, Saikia Pahi, Varughese Anil, Semetko Holli A, Kumar Anup
University of Exeter.
Indian Institute of Technology Guwahati.
Polit Psychol. 2021 Oct;42(5):827-844. doi: 10.1111/pops.12774. Epub 2021 Jul 18.
Drawing on social identity theory and research on digital media and polarization, this study uses a quasi-experimental design with a random sample ( = 3304) to provide causal evidence on perceptions of who is to blame for the initial spread of COVID-19 in India. According blame to three different social and political entities-Tablighi Jamaat (a Muslim group), the Modi government, and migrant workers (a heterogeneous group)-are the dependent variables in three OLS regression models testing the effect of the no-blame treatment, controlling for Facebook use, social identity (religion), vote in the 2019 national election, and other demographics. Results show respondents in the treatment group were more likely to allay blame, affective polarization (dislike for outgroup members) was social identity based, not partisan based, and Facebook/Instagram use was not significant. Congress and United Progressive Alliance voters in 2019 were less likely to blame the Modi government for the initial spread. Unlike extant research in western contexts, affective and political polarization appear to be distinct concepts in India where social identity complexity is important. This study of the first wave informs perceptions of blame in future waves, which are discussed in conclusion along with questions for future research.
借鉴社会认同理论以及关于数字媒体和两极分化的研究，本研究采用准实验设计和随机样本（(n = 3304)），以提供关于印度新冠疫情最初传播应由谁负责的看法的因果证据。将责任归咎于三个不同的社会和政治实体——塔布里希教团（一个穆斯林团体）、莫迪政府和农民工（一个异质群体）——是三个OLS回归模型中的因变量，这些模型测试了无责任归因处理的效果，并控制了脸书使用情况、社会认同（宗教）、2019年全国选举中的投票情况以及其他人口统计学因素。结果显示，处理组中的受访者更有可能减轻指责，情感两极分化（对外群体成员的厌恶）基于社会认同而非党派，并且脸书/照片墙的使用并不显著。2019年的国大党和联合进步联盟选民不太可能将疫情最初传播归咎于莫迪政府。与西方背景下的现有研究不同，在印度，情感两极分化和政治两极分化似乎是不同的概念，社会认同的复杂性在其中很重要。对第一波疫情的这项研究为未来几波疫情中的责任认知提供了参考，结论部分讨论了这一点以及未来研究问题。
