Didn’t you see my message?: Predicting attentiveness to mobile instant messages

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 l...

Full description

Saved in:
Bibliographic Details
Main Authors: PIELOT, Martin, DE OLIVEIRA, Rodrigo, KWAK, Haewoon, OLIVER, Nuria.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/6094
https://ink.library.smu.edu.sg/context/sis_research/article/7097/viewcontent/2556288.2556973.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-7097
record_format dspace
spelling sg-smu-ink.sis_research-70972021-09-29T12:45:58Z Didn’t you see my message?: Predicting attentiveness to mobile instant messages PIELOT, Martin DE OLIVEIRA, Rodrigo KWAK, Haewoon OLIVER, Nuria. 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%. 2014-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6094 info:doi/10.1145/2556288.2556973 https://ink.library.smu.edu.sg/context/sis_research/article/7097/viewcontent/2556288.2556973.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University prediction attentiveness messaging asynchronous communication availability mobile devices Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic prediction
attentiveness
messaging
asynchronous communication
availability
mobile devices
Theory and Algorithms
spellingShingle prediction
attentiveness
messaging
asynchronous communication
availability
mobile devices
Theory and Algorithms
PIELOT, Martin
DE OLIVEIRA, Rodrigo
KWAK, Haewoon
OLIVER, Nuria.
Didn’t you see my message?: Predicting attentiveness to mobile instant messages
description 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%.
format text
author PIELOT, Martin
DE OLIVEIRA, Rodrigo
KWAK, Haewoon
OLIVER, Nuria.
author_facet PIELOT, Martin
DE OLIVEIRA, Rodrigo
KWAK, Haewoon
OLIVER, Nuria.
author_sort PIELOT, Martin
title Didn’t you see my message?: Predicting attentiveness to mobile instant messages
title_short Didn’t you see my message?: Predicting attentiveness to mobile instant messages
title_full Didn’t you see my message?: Predicting attentiveness to mobile instant messages
title_fullStr Didn’t you see my message?: Predicting attentiveness to mobile instant messages
title_full_unstemmed Didn’t you see my message?: Predicting attentiveness to mobile instant messages
title_sort didn’t you see my message?: predicting attentiveness to mobile instant messages
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/6094
https://ink.library.smu.edu.sg/context/sis_research/article/7097/viewcontent/2556288.2556973.pdf
_version_ 1770575819006017536