The accurate judgment of social network characteristics in the lab and field using thin slices of the behavioral stream

• Published in: Organizational behavior and human decision processes Vol. 168; p. 104103
• Main Authors: Mobasseri, Sanaz, Stein, Daniel H., Carney, Dana R.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.01.2022
• Subjects: Accuracy; Person perception; Personality; Social behavior; Social judgment; Social networks; Social perception; Thin slices; Accuracy; Social behavior; Thin slices; Social perception; Social networks; Personality; Person perception; Social judgment
• ISSN: 0749-5978; 1095-9920
• DOI: 10.1016/j.obhdp.2021.09.002

•Findings provide first evidence that people can accurately infer social network characteristics of strangers.•Multiple studies consistently show that network size, gender composition (proportion female vs. male), and family composition (proportion family members vs. friends/colleagues) can be accurately inferred from brief videos or live interactions.•Findings illustrate some of the social behaviors that help or hinder accuracy in social network judgment—contributing to theory in organizational behavior, sociology, network science, and social psychology.•A standardized test of social network accuracy (SNAT) is freely available to researchers. All data, videos, and materials are on OSF (https://osf.io/zgbse). When deciding whom to ally with or avoid, people benefit from assessing the quantity and quality of strangers’ relationships with others. How accurately do people make such social network assessments? Across three lab studies and one preregistered field study, we tested whether people (total N = 1545) could make accurate judgments about a stranger’s (total N = 709) social network characteristics after watching brief thin slice videos of the stranger or negotiating with them. The findings consistently demonstrated that perceivers accurately detected the size of a stranger’s social networks and their gender and family composition, based on theoretically relevant social-behavioral tendencies and traits (e.g., extraversion, gender), but not how interconnected these social networks were. Perceivers also missed cues that could have facilitated greater accuracy. These data advance theory about adaptive social decision making in psychology, network science, sociology, and organizational behavior. We also provide the freely available Social Network Accuracy Test (SNAT) for future research: (https://osf.io/zgbse).