UrbanSocialRadar: A place-aware social matching model for estimating serendipitous interaction willingness in Korean cultural context

Abstract

Abstract In the era of perpetual digital connectedness, information and communication technology has significantly altered the way people communicate and interact with each other. Nonetheless, the computer-mediated communication should only complement offline communication rather than substituting it, as the resultant online ties are not as strong as face-to-face ties. In an effort to understand the motives in making offline social interactions real and ultimately to predict willingness to engage in serendipitous interactions with people encountered in a public place, we propose a place-aware social matching model driven by interpersonal factors (i.e., similarity, complementarity, and intimacy) and socio-spatial factors (i.e., place sociability, information acquisition expectancy, and perceived personal space in a place). Through a web-based social matching survey experiment (N = 1139 matches from 99 participants in Korea) based on a bogus stranger paradigm, we examine the interrelationship between those factors and the interaction willingness using a series of multiple regression analyses and build a prediction model by devising predictive features based on several machine learning models. From this, we find that both factors have statistically significant influence on interaction willingness, yet interpersonal factors have a higher relative importance than the socio-spatial factors. The interesting point is that the predictive power of these factors varies according to the place characteristics and the level of interaction willingness. We also empirically test the predictability of the model built from the controlled lab experiment through real-world experiments. The results reveal that the proposed model predicts interaction willingness in a real world with under 21% error rate within the Korean cultural context. Findings have implications for the design of mobile social networking systems that endeavor to facilitate serendipitous interactions.