What We Use to Predict a Mobile-Phone Users´ Status in Campus?

Mobile phones are quickly becoming the primary source for social and behavioral sensing and data collection. A great deal of research effort in academia and industry is put into mining this data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and so on. In this work, we have an attempt to predict a user´s status in campus, such as teacher and student. We focus on comparing the difference among voice, message, and stream which we use to predict a user is a teacher or student. Result show that when we use voice, message or stream separately to predict, the results have obvious differences.