

Predicting Attentiveness to Mobile Instant Messages (ACM CHI 2014) [pdf] - jcr
http://pielot.org/pubs/Pielot2014-CHI-AttPred.pdf

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jcr
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%"_

Thirty Second Video for ACM CHI 2014

[https://www.youtube.com/watch?v=gbyxOHZF1E8](https://www.youtube.com/watch?v=gbyxOHZF1E8)

ACM CHI 2014 Paper Citation:

[http://dl.acm.org/citation.cfm?doid=2556288.2556973](http://dl.acm.org/citation.cfm?doid=2556288.2556973)

