Predicting Selective Availability for Instant Messaging

Instant messaging (IM) systems allow users to spontaneously communicate over distance, yet they bear the risk for disruption of the recipient. In order to reduce disruption, novel approaches for detecting and presenting mutual availability are needed. In this paper we show how fine-grained IM availability predictions can be made for nomadic users solely based on sensors installed on a laptop computer. Our approach provides comparable accuracies to previous work, while it eliminates the need for augmenting the offices or the users with further sensors. We performed a user study to collect sensor data. Alongside with labels collected by means of Experience Sampling, the data allow for creating probabilistic models for predicting selective availability. This way, we demonstrate how the required effort involved in proactively managing one’s availability selectively towards a variety of recipients can be reduced by automatic adaptation, and give insights in the lessons learned.