Big Data and Computational Social Science Lecture: James Kitts

Friday May 17, 2019
11:30 AM - 01:00 PM
ANSO 2107

A Tale of Two Revolutions: Rethinking Social Networks in the Era of Big Brother

Abstract: Social network analysis has proliferated throughout the social sciences over the past 50 years. In a recent paper I have argued that this work has conceptualized ‘social ties’ in four fundamentally different ways – as socially constructed role relations such as friendship or co-authorship; interpersonal sentiments such as liking or hatred; behavioral interactions such as communication or scholarly citations; or access to information or other resources. In this presentation I will discuss the interplay of these concepts, consider where ties (and non-ties) are likely to match across these four domains, and thus assess where we may apply theories based on one network concept (e.g., sentiment ties of liking and disliking) to data representing another (e.g., interaction as logs of e-mails sent). I will briefly describe my current study of adolescent health behavior that empirically validates these distinctions by measuring peer networks in all four ways simultaneously. Then I will discuss some empirical lenses emerging from computational social science, such as wearable sensors, location-aware devices, online calendars, logs of phone calls, e-mails, or online transactions. I will ask how these time-stamped event series correspond to the social science concepts of social networks above and call for a new analytical approach: Directly theorizing and analyzing the structural-temporal interdependencies of interaction events redirects our attention from structural patterns to social processes. This talk is a theoretical working paper integrating and extending the insights from a conceptual article in Advances in Group Processes and an empirical study in the American Journal of Sociology.

James Kitts is a professor of sociology and director of the Computational Social Science Institute at the University of Massachusetts. He earned his Ph.D. from Cornell University in 2001 and previously held faculty appointments at Columbia University, Dartmouth College, and the University of Washington. Bridging computational social science, sociology, and public health, James has worked on methods for detecting networks of social interaction using wearable sensors, analyzed the network dynamics of adolescent friendships and inter-hospital patient transfers, modeled opinion polarization on influence networks, and conducted field research on diet norms in networks of militant vegans. He is Principal Investigator on an NIH R01 grant investigating the diffusion of health behavior on adolescent social networks in four urban middle schools.

This lecture is part of a series organized by the UBC Big Data and Computational Social Science Research Cluster. This talk is co-sponsored with the Department of Sociology and the Department of Statistics.