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Computational Social Science: where are we now? : Reimaging theories and methods to understand and enable the algorithmically infused changing nature of work

Speakers:

Bio: Noshir Contractor is the Jane S. & William J. White Professor of Behavioral Sciences in the McCormick School of Engineering & Applied Science, the School of Communication and the Kellogg School of Management and Director of the Science of Networks in Communities (SONIC) Research Group at Northwestern University. He is also the President-Elect of the International Communication Association (ICA). He is a Fellow of the American Association for the Advancement of Science (AAAS), the Association for Computing Machinery (ACM), and the International Communication Association (ICA). He also received the Distinguished Scholar Award from the National Communication Association and the Lifetime Service Award from the Organizational Communication & Information Systems Division of the Academy of Management. He was selected as the recipient of the 2022 Simmel Award from the International Network for Social Network Analysis. In 2018 he received the Distinguished Alumnus Award from the Indian Institute of Technology, Madras where he received a Bachelor’s in Electrical Engineering. He received his Ph.D. from the Annenberg School of Communication at the University of Southern California.

Computational social science offers the potential to reimagine communication theories and methods to understand and enable the algorithmically infused changing nature of work. Researchers have heralded for decades the potential of social network analysis to focus not only on who people are but also who they know. Social network analysis can be used to identify “high potentials;” who has good ideas; who is influential; what teams will get work done efficiently and effectively is well established based on decades of research. The challenge has been the collection of network data via surveys that are time consuming; elicit low response rates and have a high obsolescence. This talk presents empirical examples ranging from corporate enterprises to simulated long duration space exploration to demonstrate how we can mine “digital exhaust”— data created by individuals every day in their algorithmically infused digital transactions; such as recommendations; newsfeeds; chats; “likes;” “follows;” @mentions; and file collaboration — to address challenges they face with issues such as team conflict; team assembly; diversity and inclusion; succession planning; and post-merger integration.