User modeling and usage profiling based on temporal posting behavior in OSNs

Abstract

In this paper, we study the posting behavior of online social network (OSN) users, in particular the posting frequency and temporal patterns, and consider possible interpretations of how users use the platform. At the aggregate (macro) level, we find two distinct peaks of traffic, one during morning working hours, and one in the evening. The morning peak is more pronounced for frequent posters, while the evening peak is pronounced for the remaining users. We postulate that this difference results from different usage purposes of the OSN platform (e.g. for work, with customers, etc.) than purely social interactions (e.g., friends, family, etc.). We also study user posting behavior at an individual (micro) level and model the user posting sequences as generated by a Hidden Markov Model. We compare the results of using a simple zeroth order model (which is equivalent to a topic model such as LDA), and a first-order model, in terms of their effectiveness in clustering and predicting user types, and show the advantage gained by the first-order HMM. Overall, our study provides new insights into user activity in today’s OSNs, and suggests a framework for profiling users based on their posting activities. We believe our approach will complement other methods of user profiling based on static demographic information and friendship network information.