Estimating users' mode transition functions and activity levels from social media

Abstract

We present a temporal model of individual-scale social media user behavior, comprising modal activity levels and mode switching patterns. We show that this model can be effectively and easily learned from available social media data, and that our model is sufficiently flexible to capture diverse users' daily activity patterns. In applications such as electric power load prediction, computer network traffic analysis, disease spread modeling, and disease outbreak forecasting, it is useful to have a model of individual-scale patterns of human behavior. Our user model is intended to be suitable for integration into such population models, for future applications of prediction, change detection, or agent-based simulation.