Quantitative social media research has traditionally been conducted from what might be called a platform-centric view, wherein researchers sample, collect, and analyzed data based on one or more topic- or user-specific keywords. Such studies have yielded many valuable insights, but they convey little about individual users’ tailored social media environments—what I call the user-eye view. Studies that investigate social media from a user-eye view tend to be rare because of the expense involved and a limited number of suitable tools. This talk introduces PIEGraph, a novel system for user-eye view research that offers key advantages over existing systems. PIEGraph is lightweight, scalable, open-source, OS-independent, and collects data viewable from mobile and desktop interfaces directly from APIs. The system incorporates an extensible taxonomy that allows for straightforward classification of a wide range of political, social, and cultural phenomena. The presentation will focus on how our research team is using PIEGraph to examine users’ potential levels of exposure to high- and low-quality information sources across the ideological spectrum. I will pay particular attention to how such exposure may be unevenly distributed across lines of race and gender.
Deen Freelon is an associate professor at the UNC Hussman School of Journalism and Media at the University of North Carolina and a principal researcher at the Center for Information, Technology, and Public Life (CITAP). His theoretical interests address how ordinary citizens use social media and other digital communication technologies for political purposes, paying particular attention to how identity characteristics (e.g. race, gender, ideology) influence these uses. Methodologically, he is interested in how computational research techniques can be used to answer some of the most fundamental questions of communication science. Freelon has worked at the forefront of political communication and computational social science for over a decade, coauthoring some of the first communication studies to apply computational methods to social media data. Computer programming lies at the heart of his research practice, which generates novel tools (and sometimes methods) to answer questions existing approaches cannot address. He developed his first research tool, ReCal, as part of his master’s thesis, and it has since been used by tens of thousands of researchers worldwide.
Part of the Good Systems Speaker Series.