Socially-Aware Virtual Agents: Automatically Assessing Dyadic Rapport from Temporal Patterns of Behavior

This work focuses on data-driven discovery of the temporally cooccurring and contingent behavioral patterns that signal high and low interpersonal rapport. We mined a reciprocal peer tutoring corpus reliably annotated for nonverbals like eye gaze and smiles, conversational strategies like self-disclosure and social norm violation, and for rapport (in 30 second thin slices). We then performed a fine-grained investigation of how the temporal profiles of sequences of interlocutor behaviors predict increases and decreases of rapport, and how this rapport management manifests differently in friends and strangers. We validated the discovered behavioral patterns by predicting rapport against our ground truth via a forecasting model involving two-step fusion of learned temporal associated rules. Our framework performs significantly better than a baseline linear regression method that does not encode temporal information among behavioral features. Implications for the understanding of human behavior and social agent design are discussed.