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