Date of Award
May 2019
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Political Science
Advisor(s)
Brian Taylor
Keywords
Authoritarianism, China, Efficacy, Information, Protest, Survey Experiment
Subject Categories
Social and Behavioral Sciences
Abstract
This project explores how protest messages affect audiences' decision to join policy oriented protests in an authoritarian context. By proposing an information model, I argue that citizens' participation is affected by the behaviors of the government and the protesters included in the protest message. Such effects are moderated by (1) the partially free media environment that selectively displays certain behaviors and hides the others; and (2) individuals' personal attributes that influences their interpretation of the messages. I used a survey experiment and a comparative text analysis of social media posts and news articles to test the information model. I found that government concession (responsiveness) can produce positive effects on audiences' participation willingness while protesters' violence generates negative effects. The propaganda media outlets selectively highlight government responsiveness in news about domestic protests so that, counter-intuitively, they become more mobilizing than non-propaganda outlets. Moreover, citizens' high government trust lead them to pay more attention to the government behaviors, while low trust lead them to be more susceptible to protesters' behaviors. Finally, the government repression remains uninfluential at this information level. These findings explain how citizens decide to participate by perceiving the macro socio-political conditions. It also explains the mechanism that protests diffuse at the individual level. Finally, it contributes to our understanding of ``the dictator's dilemma'' between responsiveness and increasing social demands in autocracies.
Access
Open Access
Recommended Citation
Shao, Li, "The Power of Protest Messages: An Information Model on Protest Participation in China}" (2019). Dissertations - ALL. 1044.
https://surface.syr.edu/etd/1044