Profiling OSN Users Based on Temporal Posting Patterns

In this paper, we study the posting behavior of 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, 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 in the remaining users. We postulate that this difference results from qualitatively different usage 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 apply LDA to cluster user temporal patterns, interpret our results. Our study provides possibly new insights into user activity in today's OSNs, and suggests a framework for profiling users based on their posting activities. In the process, we provide a novel application of LDA, to temporal user posting behavior by equating the time epochs of posts to words in documents. We believe our approach will complement other methods of user profiling based on static demographic information and friendship network information.