Interaction of information flows with dynamic networks
Jeff Hemsley - doctoral candidate at the Information School, University of Washington
The diffusion of information can have both positive and negative impacts on commerce, force public officials out of office, and connect people with shared interests. The distributed nature of our digital social networks means that mainstream media and governments have less control over the flow of information and that networks of like-minded individuals can quickly coalesce around issues and grievances to engage in collective action. Although researchers have studied how different network models (e.g. small world (Watts, 2004), preferential attachment (Barabasi, 2003)) affect the flow of information and the relationship between the flow of information and personal influence, few have examined how the specific paths of these flows affect the speed and reach of the information and how these flows and paths can subsequently restructure the network, facilitating the emergence of collective action networks. Current studies have not offered a method for analyzing information flows that can identify those specific flows that are likely to alter network structures.
This dissertation seeks to address these gaps. It proposes a novel approach to measuring changes in network structures and to identifying information flows associated with these changes. This approach is demonstrated using Twitter data drawn from the Occupy Wall Street Movement. The analysis employs social network analysis and regression techniques to differentiate and understand characteristics of these flows and their relationships to changes in the network structure. The findings from this work will provide network scholars with insight into how information flows are shaped by, and in turn shape, the social networks that connect humans, organizations and institutions. Additionally, methods developed in this research can inform future studies by providing an empirical basis for distinguishing between network-altering flows and non-altering flows.
Jeff Hemsley is a PhD candidate in the Information School (iSchool) at the University of Washington. He is a computational social scientist, drawing on theories from sociology and communication to study social media. His current research looks at information flows in social media networks, with an emphasis on social movements and political events. He builds tools that collect, curate, visualize and analyze big data sets. He combines social network analysis, econometrics techniques, and computational simulation methods in addressing research questions.
Recent research includes the examination of Twitter users’ relationship to place as a factor in the formation of contentious political networks (Hemsley & Eckert, 2014) and the linking behavior of influential political blogs when linking to viral political videos (Nahon & Hemsley, 2013). He is co-author of the book Going Viral (Polity Press, 2013), which explains what virality is, how it works technologically and socially, and draws out the implications of this process for social change. He is a founding member of the Social Media Lab @ UW, which has received RAPID and INSPIRE awards from NSF, an Amazon Web Services in Education research grant award, and a gift from Microsoft Research to support this research.