
Contents
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Computational Thinking about Social Processes Computational Thinking about Social Processes
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Challenges in Modeling Social Data Challenges in Modeling Social Data
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Machine Learning and Social Sciences Machine Learning and Social Sciences
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Online Experimentation on Interactions Online Experimentation on Interactions
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Online Field Experiments Online Field Experiments
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Challenges Challenges
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Notes Notes
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References References
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28 Computational Social Science, Big Data, and Networks
Get accessBruno Abrahao is an Assistant Professor of Information Systems and Business Analytics, NYU Shanghai, and Global Network Assistant Professor, New York University. His research focuses on theoretical and applied aspects of data science and machine learning to investigate social behavior. Abrahao holds a PhD in Computer Science from Cornell University, was a Postdoctoral Fellow at Stanford University, with affiliations in the Computer Science and Sociology departments, and was a Postdoctoral Researcher at Microsoft Research AI, Redmond.
Paolo Parigi is a researcher at Facebook. He is interested in trust and in the broader area of how technology is impacting relationships. The key insight of his work is that technology is not only accumulating data about people but is transforming lives. We live in a largely engineered space where interactions are often designed by algorithms. A new space for an applied social science has now emerged as a result of the digital transformation. Paolo’s current position in industry allows him to pursue this more applied side of computational social science. Prior to his current position, Paolo has worked as an assistant professor at Stanford, senior data scientist at Uber and lead trust scientist at Airbnb.
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Published:15 December 2020
Cite
Abstract
The emergence of Big data and a quantified social space has prompted the birth of a new science, computational social science (CSS), whose roots are founded in research aiming to describe social processes using computational models. Big data now fuels rapid advancements in the field, providing the basis for building models and algorithms of human behavior. New sources of massive amounts of data fundamentally reflect interactions, and, in this context, networks are intuitive abstractions to model our social life, especially that mediated by technology. The chapter introduces several examples of empirical and theoretical CSS research employing network analysis, machine learning and online experiments. It concludes with a list of challenges confronting CSS practitioners, in and outside of academia.
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