| Low cost networked communication technologies have precipitated fundamental changes in the market forces governing the production and consumption of information. These developments, in turn, have shaped the character of political discourse, enabling citizens to engage in anonymous, homophilous, geographically-unconstrained communication with a potentially global audience. Seeking to understand the effects of these changes, this research employs the tools of complex network analysis, text mining, and machine learning to quantify the structure and dynamics of political communication in a high volume social media stream. In doing so, we shed light on the network signatures of automated propaganda campaigns, the polarized nature of domestic political communication, partisan asymmetries in online political activity, and the geospatial structure and temporal evolution of social movement communication networks. Blending quantitative results with theory from the political and social sciences, this work provides a detailed accounting of the structure and dynamics of political communication in a high profile social media stream. |
