Sensing, Understanding, and Shaping Social Behavior

Abstract

The ability to understand social systems through the aid of computational tools is central to the emerging field of computational social systems. Such understanding can answer epistemological questions on human behavior in a data-driven manner, and provide prescriptive guidelines for persuading humans to undertake certain actions in real-world social scenarios. The growing number of works in this subfield has the potential to impact multiple walks of human life including health, wellness, productivity, mobility, transportation, education, shopping, and sustenance. The contribution of this paper is twofold. First, we provide a functional survey of recent advances in sensing, understanding, and shaping human behavior, focusing on real-world behavior of users as measured using passive sensors. Second, we present a case study on how trust, which is an important building block of computational social systems, can be quantified, sensed, and applied to shape human behavior. Our findings suggest that:1) trust can be operationalized and predicted via computational methods (passive sensing and network analysis) and 2) trust has a significant impact on social persuasion; in fact, it was found to be significantly more effective than the closeness of ties in determining the amount of behavior change.