Research
How do media and civil society organizations work to mitigate misinformed prejudices and build common ground?
Uninformed and misinformed beliefs about politics and others give rise to distrust, prejudice, and violence that stabilize democracy. Some political actors generate baseless claims to instigate divisions. Many others intentionally and unintentionally consume and spread them. Still, some others work to mitigate such divisions–including civil society organizations, media, science, and government. The relationship between these opposing forces motivates my research.
Theme 1: Building Bipartisan Trust in Evidence-based Sources
My research seeks to clarify conditions under which Democrats and Republicans can converge on trusted news sources. By doing so, I aim to address the deep-seated and troubling social problem of a partisan divide over basic facts.
Dissertation: Building Bipartisan Trust in Political Fact-Checking
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“The Objectivity Dilemma in Fact-Checking: The Effects of Asymmetric Coverage on Source Credibility” (Job Market Paper, Under Review)
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U-M Center for Political Studies Blog, "Could Balanced Coverage Improve Public Trust in Fact-Checking Sites?" (Oct 17, 2022)
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"Why Favorable Views but Limited Use of Fact-Checking? Familiarity with and Trust in Fact-Checking Sites and Conventional Media.” (Under Review)
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“Achieving Democratic Accountability via Fact-Checking: The Effects of Topic Coverage Scope on Source Credibility.”
My dissertation focuses on political fact-checking, a genre of reporting dedicated to assessing the accuracy of political claims, which was initiated in the 2000s by FactCheck.org, PolitiFact, and the Washington Post’s Fact Checker. The nonpartisan, evidence-based approach of fact-checking can potentially build common factual ground across party lines. However, it remains uncertain whether fact-checking has succeeded at convincing both partisan groups. I examine obstacles to and prospects for bipartisan trust in political fact-checking by analyzing how fact-checking norms and practices affect partisans’ source assessments.
Theme 2: The Role of Shared Experiences and Emotions
Many studies have identified psychological mechanisms that reinforce partisan divisions, but less is known about what can help mitigate partisan divisions. Apart from my dissertation, I have conducted experimental studies that examine shared experiences and emotions as a potential bridge that can help partisans converge on factual perceptions and achieve democratic accountability.
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"Can Corruption Connect You to Politics? The Effects of Corruption Scandals on Blaming the Government for Personal Concerns" with Deanna Kolberg-Shah. (Revise & Resubmit at Political Psychology)
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“Disaster Experience Mitigates the Partisan Divide on Climate Change: Evidence from Texas” with Ted Hsuan Yun Chen, Christopher Fariss, and Xu Xu. (working paper available upon request)
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“Impede or Imperil? Distinguishing the Roots of Public Anger and Fear” with Ted Brader and Erin Cikanek.
Theme 3: Improving Measurement and Inferences
My secondary research interest lies in enhancing measurements and inferences in social science research. Using latent variable models, I develop and refine measurements of psychological tendencies that are not directly observable. I also examine the historical trajectory of statistical significance claims in social science in light of increasing computing power, in search of more effective inferences.
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“The Reasoning through Evidence versus Authority (EvA) Scale: Scale Development and Validation” with Priti Shah and Stephanie Preston. (Under Review)
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“Ordered Bayesian Aldrich-McKelvey Scaling: Improving Bias Correction on the Liberal-Conservative Scale” with Kevin McAlister and Erin Cikanek.
- Received the Samuel Eldersveld Outstanding Paper Award, Department of Political Science, University of Michigan. 2019.
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“What Can We Learn from Social Science’s Steroid Era? A Proposal to Reinterpret Fifty Years of Statistical Significance Claims” with Nicolás Idrobo, Arthur Lupia, and Rocío Titiunik. (working paper available upon request)