Abstract
The study of nonverbal behavior (NVB), and in particular kinesics (i.e., face and body motions), is typically seen as cost-intensive. However, the development of new technologies (e.g., ubiquitous sensing, computer vision, and algorithms) and approaches to study social behavior [i.e., social signal processing (SSP)] makes it possible to train algorithms to automatically code NVB, from action/motion units to inferences. Nonverbal social sensing refers to the use of these technologies and approaches for the study of kinesics based on video recordings. Nonverbal social sensing appears as an inspiring and encouraging approach to study NVB at reduced costs, making it a more attractive research field. However, does this promise hold? After presenting what nonverbal social sensing is and can do, we discussed the key challenges that researchers face when using nonverbal social sensing on video data. Although nonverbal social sensing is a promising tool, researchers need to be aware of the fact that algorithms might be as biased as humans when extracting NVB or that the automated NVB coding might remain context-dependent. We provided study examples to discuss these challenges and point to potential solutions.
Highlights
Investigating nonverbal behavior (NVB), and in particular kinesics, namely face and body motions used in communication (Birdwhistell, 1955; Burgoon and Dunbar, 2018), involves observing social interactions and coding movements of participants in the face and the body
We focused on kinesics and the use of nonverbal social sensing based on video recordings
When interested in developing algorithms that mimic human perception and judgment, we required human coders who are instructed and trained to perform the coding manually or naïve raters who report their perception of the targets
Summary
Investigating nonverbal behavior (NVB), and in particular kinesics, namely face and body motions used in communication (Birdwhistell, 1955; Burgoon and Dunbar, 2018), involves observing social interactions and coding movements of participants in the face and the body. Using nonverbal social sensing, when studying NVB, has the potential to reveal meaningful nonverbal patterns more (e.g., looking at the interaction partner while speaking, see Burgoon et al, 2014 for an example in detection of deception using computer-assisted coding and an algorithm to identify temporal patterns) instead of extracting only isolated NVB cues (e.g., duration of looking at the interaction partner and the number of speech turns of the target). These advantages might attract new researchers to study NVB, enriching and broadening the field. This distinction between units and inferences, between objective and subjective measurements (Burgoon and Dunbar, 2018), is key in understanding the workings and challenges of nonverbal social sensing
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