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Yuehong Cassandra Tai, Ph.D. Candidate
yhcasstai@psu.edu
Graduate Student
University of Iowa
About Me:
I am a post-doc at the Center for Social Data Analytics at Penn State University. I am interested in and curious about understanding society from big data and Non-traditional data. Prior to going back to graduate school, I worked as a deputy editor for six years in Sohu, one of the big four online portals in China. Outside of study, I enjoy being with friends and family and adventuring outdoors.请使用手机"扫一扫"x
Research Interests
Comparative Politics
Political Communication
Computational Social Science
Bayesian Inference
Machine Learning
Countries of Interest
China
United States
My Research:
My research uses quantitative and computational techniques to analyze large-scale survey data and nontraditional data. Drawing on millions of social media data, I apply machine learning and deep learning to understand dynamic communication including but not limited to misinformation dissemination and hate speech, across various media platforms. Drawing on millions of survey data, I use a Bayesian Item Response Theory (IRT) measurement model to measure latent public opinion and understand the causes and consequences of public opinion on policy outcomes in a variety of regime types.
Publications:
Journal Articles:
Prominent recent work argues that support for democracy behaves thermostatically—that democratic erosion boosts democratic support while deepening democracy yields public backlash—and further contends that there is no evidence for the classic argument that democracy itself increases democratic support over time. Here, we document how these conclusions depend on subtle choices in measurement coding that constitute “researcher degrees of freedom”: analyses employing alternative reasonable choices provide little or no support for the original conclusions. The fragility of the statistical results demonstrates that researcher degrees of freedom in measurement must be taken seriously and that the question of the relationship between democratic institutions and democratic support remains unsettled.
We study the roles of elected officials in the dissemination of misinformation on Twitter. This is a particularly salient online population since elected officials serve as primary sources of information for many stakeholders in the public, media, government, and industry. We analyze the content of tweets posted from the accounts of over 3,000 U.S. state lawmakers throughout 2020 and 2021. Specifically, we identify the dissemination of URLs linked to unreliable content. Our starkest finding is that Republicans share more misinformation than do Democrats by an order of magnitude. Additionally, we uncover distinct patterns in the temporal trends of tweets and tweets associated with misinformation across party and state lines. Delving into the content of tweets referencing unreliable URLs reveals discussions of election integrity, abortion, COVID-19 policies, and immigration. Furthermore, consistent with the literature on asymmetric polarization, Republicans exhibit a greater inclination toward engaging in partisan attacks. We also find that state lawmakers often tweet about state-specific topics. These findings enhance our understanding of misinformation, political communication, and state politics.
Do democratic regimes depend on public support to avoid backsliding? Does public support, in turn, respond thermostatically to changes in democracy? Two prominent recent studies (Claassen 2020a; 2020b) reinvigorated the classic hypothesis on the positive relationship between public support for democracy and regime survival—and challenged its reciprocal counterpart—by using a latent variable approach to measure mass democratic support from cross-national survey data. However, both studies used only the point estimates of democratic support. We show that incorporating the concomitant measurement uncertainty into these analyses reveals that there is no support for either study’s conclusion. Efforts to minimize the uncertainty by incorporating additional survey data still fail to yield evidence in support of either hypothesis. These results underscore the need for both more nuanced analyses of the relationships between public support and democracy and taking measurement uncertainty into account when working with latent variables.
During the COVID-19 crisis, what explains the variation in policy adoptionamong Chinese provincial governments? To answer this question, we gathered data on twenty-five COVID-19 containment policies used in China from 31 December 2019 to 18 March 2020. We conducted state-of-the-art multilevel pooled event history analysis to allow us to control for policy heterogeneity. Our results demonstrate that variation in policy adoption during the crisis largely follows politics as usual: policies diffuse from the centre to the provinces in the same way that non-crisis policies diffuse. Our findings highlight the political dynamics of policy adoption and crisis response within China.
Scholars who study governance in authoritarian countries have long highlighted the importance of fiscal capacity and pressure from the central government in determining the responsiveness and policy changes of subnational governments. However, policy orientation is also important in shaping how subnational governments react to a crisis. Using provincial governments’ responses during the 2018 Chinese vaccine scandal, I find strong evidence that an emphasis on public health, as well as early responses by neighboring provinces, increased the likelihood of a quick response. Moreover, issue salience minimized the direct effect of pressure from the national government. An additional paired case study of the provinces of Hubei and Hunan shows that the importance accorded by the provincial government to public health policy was implemented at the sub-provincial level through China’s one-level-down cadre management system; it also may explain Hubei’s delay in responding to COVID-19 at an early stage.