  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.
