Presented By: School of Information
Characterizing and Quantifying Public Conversations Online
Sean Taylor, Facebook social scientist
Online public conversations, which feature open participation and interactions between strangers, are increasing in their frequency and societal importance. Digital platforms such as Facebook, Twitter, and Reddit face challenges in improving these conversations, from designing user interfaces to stimulating participation and moderating or prioritizing content. Perhaps the most important challenge is deciding which kinds of conversations are most valuable to people and how the value generated can be practically measured at large scale and across a variety of use-cases.
In this talk Facebook social scientist Sean Taylor will present a range of approaches to measuring and characterizing online public conversations. In the course of product research at Facebook, they have applied machine learning, human annotation, surveys, network analysis, and ethnographic research in order to gain a deeper understanding of the space of public conversations and to try to pin down how and when they create value for the participants. He discusses how ongoing advancements in measurement technology have helped improve Facebook products, and what they have learned about human behavior in the process of studying these social systems.
About the Speaker: Sean J. Taylor is a computational social scientist on Facebook’s Data Science team. Prior to Facebook, he earned his PhD in Information Systems from NYU’s Stern School of Business. He specializes in using machine learning methods and randomized experiments for measurement, prediction, and policy decisions. Sean’s research ranges from studying online social influence, viral marketing, and social networks to measuring how sports fans behave and the impact of data science on decision making in organizations. He is also an avid engineer who enjoys putting academic research into practice by building tools and services like new kinds of prediction markets and automated forecasting systems.
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The Social, Behavioral and Experimental Economics lecture series is sponsored by the School of Information, the Ross School of Business and the Department of Economics. Speakers from U.S. and international universities present their research at weekly seminars during the 2017-2018 academic year.
In this talk Facebook social scientist Sean Taylor will present a range of approaches to measuring and characterizing online public conversations. In the course of product research at Facebook, they have applied machine learning, human annotation, surveys, network analysis, and ethnographic research in order to gain a deeper understanding of the space of public conversations and to try to pin down how and when they create value for the participants. He discusses how ongoing advancements in measurement technology have helped improve Facebook products, and what they have learned about human behavior in the process of studying these social systems.
About the Speaker: Sean J. Taylor is a computational social scientist on Facebook’s Data Science team. Prior to Facebook, he earned his PhD in Information Systems from NYU’s Stern School of Business. He specializes in using machine learning methods and randomized experiments for measurement, prediction, and policy decisions. Sean’s research ranges from studying online social influence, viral marketing, and social networks to measuring how sports fans behave and the impact of data science on decision making in organizations. He is also an avid engineer who enjoys putting academic research into practice by building tools and services like new kinds of prediction markets and automated forecasting systems.
*
The Social, Behavioral and Experimental Economics lecture series is sponsored by the School of Information, the Ross School of Business and the Department of Economics. Speakers from U.S. and international universities present their research at weekly seminars during the 2017-2018 academic year.
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