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Understanding smartphone notifications user interactions and

Contents listsavailable atScienceDirect

International Journalof Human-ComputerStudies

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Understanding smartphonenotifications" userinteractions andcontent importance 1

Aku Visuri

a , Nielsvan Berkel b , TadashiOkoshi c , JorgeGoncalves b , VassilisKostakos b a Ncanudears toifiwfil bucrraxmarudmc ymrfifl fifiwfil acwmc b u Ncanudearsto uwtfidculbmdynawwul !fl "fierdmwam c xuat Ncanudearsl# t$u%f&%&arml acmrt%yfil'ys'%rt f(%(!&l)m*mc

ARTICLE INFO

xus+tde,

Smartphone

Notifications

Interactions

Semantic analysis

Machine learning

ABSTRACT

We presentthe resultsof ourexperiment aimedto comprehensivelyunderstand thecombination of1) how

smartphone usersinteract withtheir notifications,2) whatnotification contentis consideredimportant, 3)the

complex relationshipbetween theinteraction choicesand contentimportance, andlastly 4)establish anin-

telligent methodto predictuser"s preferenceto seeingan incomingnotification. Weuse adataset ofnotifications

received by40 anonymoususers in-the-wild,which consistsof 1)qualitative user-labelledinformation about

their preferenceson notification"scontents, 2)notification source,and 3)the contextin whichthe notification

was received.We assessthe effectivenessof personalisedprediction modelsgenerated usinga combinationof

self-reported contentimportance andcontextual information.We uncoverfour distinctuser types,based onthe

number ofdaily notificationsand interactionchoices. Weshowcase howusage traitsof thesegroups highlight

the requirementfor notificationfiltering approaches,u-.- , whenspecific usershabitually neglectto manually

filter outunimportant notifications.Our machinelearning-based predictor,based onboth contextualsensing and

notification contentscan predictthe user"spreference forsuccessfully acknowledgingan incomingnotification

with 91.1%mean accuracy,crucial fortime-critical userengagement andinterventions. 1. Introduction Mobile notificationsallow applicationsto informusers ofincoming messages, newsystem events,and reminders,without requiringexplicit interaction. Usersreceive upwardsfrom 60daily notifications

Pielot etal., 2014

;Shirazi etal., 2014), ofwhich manyare considered unimportant bythe recipient.In response,researchers aimto reducethe interruptive natureof unwantednotifications (

Mehrotra etal., 2016

Okoshi etal., 2015

;Oliveira etal., 2014;Poppinga etal., 2014) via sensing technologiesor byunderstanding thequalitative natureof no- tifications. Whilea largebody ofwork existson predictingnotification- driven interruptibilitythrough situationalcontext, thesemethods failto

capture theother sideof thechallenge -is anotification importantto the user.Thus, thereis aneed forbetter understandingof there-

lationship betweeninteracting withnotifications -how userschoose to interact -and theperceived importanceof thenotification contents. Here, weaim tounderstand theunderlying importanceof individual notifications, howusers interactwith them,and whichfactors influence their interactionchoices. Toinvestigate themotivation forinteracting with notifications,we useself-reported informationabout theim- portance ofnotification contents,notification source,as wellas the context ofpresentation. Wecapture themotivation interms ofnotifi- cations theuser wouldprefer tosee regardlessof theinteraction, u-.-, notifications thatshould bepresented evenif habituallyignored or dismissed, andthe notificationsthat theuser mightconsider irrelevant

or disrupting.Our findingshighlight thediverse natureof users"https://doi.org/10.1016/j.ijhcs.2019.03.001

Received 21March 2018;Received inrevised form20 February2019; Accepted4 March2019 We herebydeclare thatall aforementionedauthors haveall madesubstantial contributionsto ourwork titled:Understanding SmartphoneNotifications" User

Interactions andContent Importance

This workhas notbeen publishedpreviously noris itcurrently underreview inany othermedium.

Our submissionconsists oftotal of11.5K wordsand isincluded inthis documentstarting frompage four.All figuresshould becolorized, whenapplicable, andare

included withinthe submission. 1

We herebydeclare thatwe, theauthors, norany correspondingfinancial orgoverning bodyaffiliated withthis submissionhave nocompeting orother interests

that mightinfluence ourwork. .

Corresponding author.

/%$maw mdueeue,Aku.visuri@oulu.fi(A. Visuri),n.vanberkel@student.uncimelb.edu.au(N. vanBerkel), slash@ht.sfc.keio.ac.jp(T. Okoshi),

jorge.goncalves@unimelbc.edu.au(J. Goncalves),vassilis.kostakos@unimeclb.edu.au(V. Kostakos). $YDLODEOHRQOLQH0DUFK quotesdbs_dbs30.pdfusesText_36
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