"Trust us": Mobile phone use patterns can predict individual trust propensity

Abstract

An individual's trust propensity - i.e., "a dispositional willingness to rely on others" - mediates multiple sociotechnical systems and has implications for their personal, and societal, well-being. Hence, understanding and modeling an individual's trust propensity is important for human-centered computing research. Conventional methods for understanding trust propensities have been surveys and lab experiments. We propose a new approach to model trust propensity based on long-term phone use metadata that aims to complement typical survey approaches with a lower-cost, faster, and scalable alternative. Based on analysis of data from a 10-week field study (mobile phone logs) and "ground truth" survey involving 50 participants, we: (1) identify multiple associations between phone-based social behavior and trust propensity; (2) define a machine learning model that automatically infers a person's trust propensity. The results pave way for understanding trust at a societal scale and have implications for personalized applications in the emerging social internet of things.