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The Center for the Study of Complex Systems pres.

CSCS/MIDAS/CSS Seminar | Towards An Artificial Intuition: Conversational Markers Of (Anti)Social Dynamics

Cristian Danescu-Niculescu-Mizil, Information Science, Cornell University

Cristian Danescu-Niculescu-Mizil Cristian Danescu-Niculescu-Mizil
Cristian Danescu-Niculescu-Mizil

Abstract: Can conversational dynamics—the nature of the back and forth between people—predict outcomes of social interactions? This talk will describe efforts on developing an artificial intuition about ongoing conversations, by modeling the subtle pragmatic and rhetorical choices of the participants.

The resulting framework distills emerging conversational patterns that can point to the nature of the social relation between interlocutors, as well as to the future trajectory of this relation. For example, I will discuss how interactional dynamics can be used to foretell whether an online conversation will stay on track or eventually derail into personal attacks, providing community moderators several hours of prior notice before an anti-social event is likely to occur.

The data and code are available through the Cornell Conversational Analysis Toolkit (ConvoKit):

This talk includes joint work with Jonathan P. Chang, Lucas Dixon, Liye Fu, Yiqing Hua, Dan Jurafsky, Lillian Lee, Jure Leskovec, Vlad Niculae, Chris Potts, Arthur Spirling, Dario Taraborelli, Nithum Thain, and Justine Zhang.

Bio: Cristian Danescu-Niculescu-Mizil is an associate professor in the information science department at Cornell University. His research aims at developing computational methods that can lead to a better understanding of our conversational practices, supporting tools that can improve the way we communicate online. He is the recipient of several awards—including an NSF CAREER Award, the WWW 2013 Best Paper Award, a CSCW 2017 Best Paper Award, and two Google Faculty Research Awards—and his work has been featured in popular media outlets such as The Wall Street Journal, NBC’s The Today Show, NPR and the New York Times.

Livestream Information

October 19, 2020 (Monday) 4:00pm
Meeting ID: 95443347994

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