Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions

TL;DR

This paper presents a computational framework for modeling face acts in persuasion conversations, enabling the identification and prediction of conversational outcomes based on face act dynamics.

Contribution

It introduces a generalized framework, coding manual, and models for face acts, revealing role-based differences and predicting key outcomes in persuasion dialogues.

Findings

Successfully identified face acts in conversations

Predicted conversational outcomes like donation success

Analyzed the impact of face acts on positive results

Abstract

The notion of face refers to the public self-image of an individual that emerges both from the individual's own actions as well as from the interaction with others. Modeling face and understanding its state changes throughout a conversation is critical to the study of maintenance of basic human needs in and through interaction. Grounded in the politeness theory of Brown and Levinson (1978), we propose a generalized framework for modeling face acts in persuasion conversations, resulting in a reliable coding manual, an annotated corpus, and computational models. The framework reveals insights about differences in face act utilization between asymmetric roles in persuasion conversations. Using computational models, we are able to successfully identify face acts as well as predict a key conversational outcome (e.g. donation success). Finally, we model a latent representation of the conversational state to analyze the impact of predicted face acts on the probability of a positive conversational outcome and observe several correlations that corroborate previous findings.