Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2014

Abstract

Mobile instant messaging (e.g., via SMS or WhatsApp) often goes along with an expectation of high attentiveness, 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 online. However, in this paper we not only provide evidence that these cues create social pressure, but that they are also weak predictors of attentiveness. As remedy, 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 behavioral data from 24 users of mobile instant messaging services. By the means of machine-learning techniques, we identified that simple features extracted from the phone, such as the user's interaction with the notification 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 selected 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%.

Keywords

prediction, attentiveness, messaging, asynchronous communication, availability, mobile devices

Discipline

Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, CHI 2014, Toronto, ON, Canada, April 26 - May 1

First Page

3319

Last Page

3328

ISBN

9781450324731

Identifier

10.1145/2556288.2556973

Publisher

ACM

City or Country

New York

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