Mobile instant messaging (e.g., via SMS or WhatsApp) often goes along with an expectation of high attentive- ness, i.e., that the receiver will notice and read the message within a few minutes. Hence, existing instant messaging services for mobile phones share indicators of availability, such as the last time the user has been on- line. However, in this paper we not only provide ev- idence that these cues create social pressure, but that they are also weak predictors of attentiveness. As rem- edy, we propose to share a machine-computed prediction of whether the user will view a message within the next few minutes or not. For two weeks, we collected behav- ioral data from 24 users of mobile instant messaging ser- vices. By the means of machine-learning techniques, we identi-ed that simple features extracted from the phone, such as the user's interaction with the noti-cation center, the screen activity, the proximity sensor, and the ringer mode, are strong predictors of how quickly the user will attend to the messages. With seven automatically se- lected features our model predicts whether a phone user will view a message within a few minutes with 70.6% accuracy and a precision for fast attendance of 81:2%.