Democratizing psychological insights from analysis of nonverbal behavior

machine learning
nonverbal behavior
big data
bayesian statistics

McDuff & Girard


Citation (APA 7)

McDuff, D., & Girard, J. M. (2019). Democratizing psychological insights from analysis of nonverbal behavior. Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction (ACII), 220–226.


The affective computing community has invested heavily in building automated tools for the analysis of facial behavior and the expression of emotion. These tools present a valuable, but largely untapped, opportunity for social scientists to perform observational analyses of nonverbal behavior at very large scale. Various tech companies are collecting huge corpora of images and videos from around the world that could be used to study important scientific questions. However, privacy restrictions and intellectual property concerns render these data inaccessible to most academics. Unfortunately, this limits the potential for scientific advancement and leads to the consolidation of data and opportunity into the hands of a few powerful institutions. In this paper, we ask whether similar psychological insights can be gained by analyzing smaller, public datasets that are more within reach for academic researchers. As a proof-of-concept for this idea, we gather, analyze, and release a corpus of public images and metadata and use it to replicate recent psychological findings about smiling, gender, and culture. In so doing, we provide evidence that psychological insights can indeed by democratized through the automated analysis of nonverbal behavior.