TreeNet analysis of human stress behavior using socio-mobile data

Abstract

Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction features and social surveys for finding human stress behavior. For this, we gathered mobile phone call logs data set containing 111,444 voice calls of 131 adult members of a living community for a period of more than 5 months. And we identified that top 5 social network measures like hierarchy, density, farness, reachability and eigenvector of individuals have profound influence on individuals’ stress levels in a social network. If an ego lies in the shortest path of all other alters then the ego receives more information and hence is more stressed. In this paper, we have used TreeNet machine learning algorithm for its speed and immunity to outliers. We have tested our results with another Random Forest classifier as well and yet, we found TreeNet to be more efficient. This research can be of vital importance to economists, professionals, analysts, and policy makers.