[Talk Summary 6] Language and Social Dynamics

Cristian Danescu-Niculescu-Mizil from University of Cornell gave the talk "Language and Social Dynamics" on September, 30, 2016 at CMU. He presented his past work including exploring the relation between users and their community, a computational framework for identifying and characterizing politeness, and predicting the future evolution of a dyadic relationship.

At the beginning, Cristian talked about the linguistic change of users when they get involved in a community. He showed that users follow a determined life-cycle regarding to the process they adopt new community norms. When taking part in a community, new members adapt to existing community norms. For a long time, members also may adapt new norms or be innovators themselves, setting new trends. Others may keep their old styles and have no reaction to the change. Those members are more likely to leave their communities. Based on this assumption, the system can predict how long they will stay active in the community.

At the second part, Cristian presented a computational framework for identifying linguistic aspects of politeness, a central force shaping our community behavior.
He gave a case study about the change of two politicians' politeness. The one who was more polite won the election, but her politeness gradually decreased after that. On the other hand, the other was more polite after the failure.  The framework presented by Cristian can be used to study the social aspects of politeness, revealing new interactions with social status and community membership.

The last past of the talk performs the prediction of the future evolution of a dyadic relationship based on conversational patterns. He provided evidence of subtle but consistent conversational patterns that foretell the betrayal in a friendship. The fact that the balance of conversational attributes such as positive sentiment, politeness, and structured discourse is suddenly changed potentially is a signal of imminent betrayal. He concluded that by exploiting these cues can help us predict imminent betrayal better than human players.

100 Porter Hall
Carnegie Mellon University


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