University of Oulu

Aku Visuri, Niels van Berkel, Tadashi Okoshi, Jorge Goncalves, Vassilis Kostakos, Understanding smartphone notifications’ user interactions and content importance, International Journal of Human-Computer Studies, Volume 128, 2019, Pages 72-85, ISSN 1071-5819,

Understanding smartphone notifications’ user interactions and content importance

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Author: Visuri, Aku1; van Berkel, Niels2; Okoshi, Tadashi3;
Organizations: 1University of Oulu, Pentti Kaiteran katu 1, 90014 Oulu, Finland
2The University of Melbourne, Parkville, VIC 3010, Australia
3Keio University, 2 Chome-15-45 Mita, Minato-ku, Tōkyō-to 108-8345, Japan
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3 MB)
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Language: English
Published: Elsevier, 2019
Publish Date: 2021-03-06


We present the results of our experiment aimed to comprehensively understand the combination of 1) how smartphone users interact with their notifications, 2) what notification content is considered important, 3) the complex relationship between the interaction choices and content importance, and lastly 4) establish an intelligent method to predict user’s preference to seeing an incoming notification. We use a dataset of notifications received by 40 anonymous users in-the-wild, which consists of 1) qualitative user-labelled information about their preferences on notification’s contents, 2) notification source, and 3) the context in which the notification was received. We assess the effectiveness of personalised prediction models generated using a combination of self-reported content importance and contextual information. We uncover four distinct user types, based on the number of daily notifications and interaction choices. We showcase how usage traits of these groups highlight the requirement for notification filtering approaches, e.g., when specific users habitually neglect to manually filter out unimportant notifications. Our machine learning-based predictor, based on both contextual sensing and notification contents can predict the user’s preference for successfully acknowledging an incoming notification with 91.1% mean accuracy, crucial for time-critical user engagement and interventions.

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Series: International journal of human-computer studies
ISSN: 1071-5819
ISSN-E: 1095-9300
ISSN-L: 1071-5819
Volume: 128
Pages: 72 - 85
DOI: 10.1016/j.ijhcs.2019.03.001
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: This work is partially funded by the Academy of Finland (Grants 286386-CPDSS, 285459-iSCIENCE, 304925-CARE, 313224-STOP), and Marie Skłodowska-Curie Actions (645706-GRAGE).
EU Grant Number: (645706) GRAGE - Grey and green in Europe: elderly living in urban areas
Academy of Finland Grant Number: 286386
Detailed Information: 286386 (Academy of Finland Funding decision)
285459 (Academy of Finland Funding decision)
304925 (Academy of Finland Funding decision)
313224 (Academy of Finland Funding decision)
Copyright information: © 2019 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license