This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.
To repress growing regional/national identity in Taiwan, China applies rationalist strategies, including economic incentives and military threats. Analysis of the Taiwan National Security Survey in 2003–2015 shows that China’s carrot and stick policies negatively correlate with exclusive Taiwanese identity. In younger generations, perception of the strength of the policies is similar, but their effect on identity is weaker.
Are Taiwanese voters polarized? By presenting four novel visualizations based on a factor analysis of Taiwan National Security Surveys from 2002 to 2017, this article describes the contours of structural change in Taiwan politics. Overall, the cross-strait position among Taiwanese voters can be described by a stable inverted U shape over time. This arises from the fact that most nonpartisans—typically neglected in the literature on polarization—are moderate. Before 2008, increasing polarization among partisans can be attributed to pan-green voters moving toward independence. Between 2008 and 2014, decreasing polarization stems from moderates self-identifying as pan-blue supporters. Since 2014, a record-breaking number of nonpartisans have left the pan-blue camp, and more extreme pan-blue voters have contributed to a return of polarization among partisans. The results yield important implications for the study of polarization and populism, as well as for the future of Taiwanese politics.
The literature on China’s social media foreign propaganda mostly focuses on text-format contents in English, which may miss the real target and the tool for analysis. In this article, we traced 1256 Twitter accounts echoing China government’s #USAVirus propaganda before and after Twitter removed state-linked operations on June 12, 2020. The 3567 tweets with #USAVirus we collected, albeit many written in English, 74% of them attached with a lengthy simplified Chinese text-image. Distribution of the post-creation time fits the working-hour in China. Overall, 475 (37.8%) accounts we traced were later suspended after Twitter’s disclosure. Our dataset enables us to analyze
why and why not
Twitter suspends certain accounts. We apply the decision tree, random forest, and logit regression to explain the suspensions. All models suggest that the inclusion of a text-image is the most important predictor. The importance outweighs the number of followers, engagement, and the text content of the tweet. The prevalence of simplified Chinese text-images in the #USAVirus trend and their impact on Twitter account suspensions both evidence the importance of text-image in the study of state-led propaganda. Our result suggests the necessity of extracting and analyzing the content in the attached text-image.
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