Inferring Individual Social Capital Automatically via Phone Logs

Abstract

Social capital is one of the most fundamental concepts in social computing. Individual social capital is often connected with one's happiness levels, well-being, and propensity to cooperate with others. The dominant approach for quantifying individual social capital remains self-reported surveys and generator-methods, which are costly, attention-consuming, and fraught with biases. Given the important role played by mobile phones in mediating human social lives, this study explores the use of phone metadata (call and SMS logs) to automatically infer an individual's social capital. Based on Williams' Social Capital survey as ground truth and ten-week phone data collection for 55 participants, we report that (1) multiple phone-based social features are intrinsically associated with social capital; and (2) analytics algorithms utilizing phone data can achieve high accuracy at automatically inferring an individual's bridging, bonding, and overall social capital scores. Results pave way for studying social capital and its temporal dynamics at an unprecedented scale.