Are You There?: Identifying Unavailability in Mobile Messaging

Delays in response to mobile messages can cause negative emotions in message senders and can affect an individual's social relationships. Recipients, too, feel a pressure to respond even during inopportune moments. A messaging assistant which could respond with relevant contextual information on behalf of individuals while they are unavailable might reduce the pressure to respond immediately and help put the sender at ease. By modelling attentiveness to messaging, we aim to (1) predict instances when a user is not able to attend to an incoming message within reasonable time and (2) identify what contextual factors can explain the user's attentiveness---or lack thereof---to messaging. In this work, we investigate two approaches to modelling attentiveness: a general approach in which data from a group of users is combined to form a single model for all users; and a personalized approach, in which an individual model is created for each user. Evaluating both models, we observed that on average, with just seven days of training data, the personalized model can outperform the generalized model in terms of both accuracy and F-measure for predicting inattentiveness. Further, we observed that in majority of cases, the messaging patterns identified by the attentiveness models varied widely across users. For example, the top feature in the generalized model appeared in the top five features for only 41% of the individual personalized models.