Doctoral Dissertation Research: Negotiation between State and Individuals through Social Media

在这个项目中，它将研究如何专制政权可以通过强制性和非强制性的媒体控制策略相结合，在社会媒体时代的意识形态危机中生存。对民主感兴趣的学者观察到社交媒体的增长，他们认为社交媒体是自由沟通和民主化的潜在来源，特别是在专制国家。然而，在许多情况下，专制政府能够维持其立场，甚至利用社交媒体加强其立场。 这项拟议中的研究将通过调查使用非强制性手段将公众注意力重新导向官方批准的方向的潜力，揭示专制弹性的难题。研究结果将有助于进一步理解发展，特别是采用新的信息和通信技术，与政治变革之间的联系，为国家安全做出贡献。本研究以一个重要案例为中心，提出了两个主要问题：1）官员推动的民主浪潮如何影响了基于微博的公共领域的民主讨论？2)谁在赢得官民之间的意识形态之争？ 这些问题将通过对2009年至2018年关键词为“民主”的微博帖子和被审查的内容进行纵向分析来解决。计算机辅助文本分析技术，以及更传统的内容分析方法，将用于发现文本中的主题，构建语义网络，并比较由国家机构，个人用户和审查用户生成的内容。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

In this project it will be investigated how authoritarian regimes can survive ideological crisis in the social media era through the combination of coercive and non-coercive media control strategies. Scholars interested in democracy have observed the growth of social media with anticipation as they imagined it to be a potential source of free communication and democratization, particularly in authoritarian states. However, in many cases, authoritarian governments have been able to maintain their positions or even to use social media to strengthen them. This proposed research will shed light on the puzzle of authoritarian resilience by investigating the potential for using non-coercive means to re-orient public attention in officially approved directions. Findings will contribute to national security by furthering understanding of the connection between development, in particular, adoption of new information and communication technology, and political change.Focusing on one important case, two main questions are asked in this research: 1) How a pro-democracy wave furthered by officials has affected discussion on democracy in the microblog-based public sphere? 2) Who is winning the ideological battle between the officials and the public? These questions will be addressed through a longitudinal analysis of microblog posts with the keyword "democracy" from 2009 to 2018 and contents that have been censored. Computer-aided text analytic techniques, alongside more conventional content analysis methodologies, will be used for discovering topics within the text, constructing semantic networks and comparing the contents generated by the state agency, the individual user and the censored user.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.