Social Reality Mining

Social Behavior Predicts Spending Behavior

Human spending behavior is essentially social. This work motivates and grounds the use of mobile phone based social interaction features for classifying spending behavior. Using a data set involving 52 adults (26 couples) living in a community for over a year, we find that social behavior measured via face-to-face interaction, call, and SMS logs, can be used to predict the spending behavior for couples in terms of their propensity to explore diverse businesses, become loyal customers, and overspend. Our results show that mobile phone based social interaction patterns can provide more predictive power on spending behavior than personality based features. Interestingly, we find that more social couples also tend to overspend. Obtaining such insights about couple level spending behavior via novel social-computing frameworks can be of vital importance to economists, marketing professionals, and policy makers.

Relevant publications:

1. V. K. Singh*, L. Freeman*, B. Lepri, and A. Pentland, Classifying Spending Behavior using Socio-Mobile Data, ASE Human Journal, 2013.
2. V. K. Singh*, L. Freeman*, B. Lepri, and A. Pentland, Social Behavior Predicts Spending Behavior, ASE/IEEE Social Computing conference, (SocialCom, 2013).
(* denotes equal contribution)
A short video presentation is available at the Social Physics website.

Influencing Behavior Change via Trusted Ties

We present a case study on how trust, 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 change1.

Relevant publications:

1. V.K. Singh, E. Shmueli, B. lepri, A. Pentland, Sensing, understanding, and shaping human behavior, IEEE Transactions on Computational Social Systems,2013 (Under submission). * indicates equal contribution.

Social Behavior Predicts Happiness and Productivity

Can social behavior measured via mobile phones be used to predict happiness and productivity levels in a community. What factors are most significantly correlated with happiness levels? Look out for more details coming soon.

Relevant publications:

1. V. K. Singh, P. Krafft, and A. Pentland, Predicting Happiness using Socio-Mobile Data, Symposium on Success, Ignite talk, ISQSS, Harvard University, 2013.