Statistical Verification of Computational Rapport Model

TL;DR

This paper applies structural equation modeling to verify and improve a computational rapport model by incorporating additional nonverbal behaviors, enhancing understanding of rapport management in conversations.

Contribution

It provides the first solid verification of a computational rapport model using SEM and introduces modifications to include gaze and smile behaviors.

Findings

Original model had unfavorable paths.

Modified model shows better fit indices.

Enhanced model offers deeper insights into rapport management.

Abstract

Rapport plays an important role during communication because it can help people understand each other's feelings or ideas and leads to a smooth communication. Computational rapport model has been proposed based on theory in previous work. But there lacks solid verification. In this paper, we apply structural equation model (SEM) to the theoretical model on both dyads of friend and stranger. The results indicate some unfavorable paths. Based on the results and more literature, we modify the original model to integrate more nonverbal behaviors, including gaze and smile. Fit indices and other examination show the goodness of our new models, which can give us more insight into rapport management during conversation.