With the rise of the Internet, smartphones, and social media, social science is becoming increasingly computational and often involves the collection and analysis of massive data.  This computational trend is reflected in my dissertation.  For me, one purpose of such an approach is to understand how Chinese netizens, who number more than 700 million, respond to an unprecedented political event, namely, the most recent anticorruption campaign.  My analysis follows two paths.  One path makes possible the identification of urban land use at a community level.  Mapping the distribution of netizens onto different communities allows me to gain more precise knowledge of their social status, means of communication, and social behavior.  A second analytical path complements the first and takes us further into prcise knowledge by graphing, following each official news announcement, human interaction at a micro-spatial granularity as well as temporally, from day to day and even moment to moment.  My novel approach uses massive social-media data.  I intend to apply to other socio-political issues in my disseration.  Its merit lies in that it offers a departure from and a check on the more common sampling, "snapshot" approach in the social sciences.