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Michigan Institute for Data Science pres.

Towards an Artificial Intuition: Conversational Markers of (Anti)Social Dynamics

Cristian Danescu-Niculescu-Mizil

https://umich.zoom.us/j/95443347994 https://umich.zoom.us/j/95443347994
https://umich.zoom.us/j/95443347994
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): http://convokit.cornell.edu
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.
https://umich.zoom.us/j/95443347994 https://umich.zoom.us/j/95443347994
https://umich.zoom.us/j/95443347994
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