Mining Social Relationship Types in an Organization using Communication Patterns

Abstract

Our goal is to show that it is possible to automatically infer social relationship types among people who stay together in an organization by analyzing communication patterns. We collected indoor co-location data and instant messenger data from 22 participants for one month. Based on the data, we designed and explored several indicators which are considered to be useful for mining social relationship types. We applied machine learning techniques using the indicators and found that it is possible to develop an intelligent method to infer social relationship types.