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1、ComputersinHumanBehavior43(2015)15–27ContentslistsavailableatScienceDirectComputersinHumanBehaviorjournalhomepage:www.elsevier.com/locate/comphumbehAnalyzingnegativeSNSbehaviorsofelementaryandmiddleschoolstudentsinKoreaSoojungLee⇑Dept.ofComputerEducation,GyeonginNationalUniv.ofEducatio
2、n,155Sammak-ro,Man-an-gu,Anyang,Gyeonggi-do,RepublicofKoreaarticleinfoabstractArticlehistory:WiththegrowingspeedofSNSacceleratedrecently,thereareincreasingconcernsaboutthepossibilityofAvailableonline16November2014youngstudentsdoingnegativeSNSbehavior.Thisstudytriedtodiscovertherelation
3、shipbetweenvar-iousvariablessuchasgender,grade,SNSusagetime,motivestouseSNS,degreeofusingadvancedfunc-Keywords:tionsofSNS,SNSdependency,andperceptionsaboutSNSwithnegativeSNSbehaviorsamongSNSelementaryandmiddleschoolstudents.Theanalysesrevealedthatregardlessofgenderandgrade,theSocialnet
4、workservicemostfrequentlycommittednegativebehavioris‘stealingothers’privateinformation’andtheleastisCyberactivity‘cyberbullying’,while‘contactingstrangers’showingthemostsignificantincreasewiththeage.WithPerceptiononSNStheseriousnessofnegativeSNSbehaviorsclassifiedintofourlevels,itwasfoun
5、dthatthebiggestsignif-MotivestouseSNSicantdifferenceacrossthelevelsappearsonillegaluseofphotosandvideos,followedbyonverbalabuseandoncyberbullying.ItwasalsofoundthatasignificantpositivecorrelationexistsbetweeneachofthefactorsconsideredandthenegativeSNSbehavior.Basedonthesefindings,aregres
6、sionanalysiswascon-ductedtosuccessfullyextractseveralfactorsassignificantpredictorsofthenegativeSNSbehaviors.Fur-thermore,factorsdistinguishingthelowestlevelfromthehighestlevelofthenegativeSNSbehaviorswereidentifiedtobegender,SNSdependency,utilizationofadvancedSNSfunctions,andgrade,withi
7、tsaccuracyratereachingashighas85.6%.Ó2014ElsevierLtd.Allrightsreserved.1.Introductionenabledinstantaneousproductionanddistributionofinformation(Jeong,2013),andsharingvariousmultimediacontentssuchasAlongwiththerecentadvancesinInformationtechnology,theimagesandvideos.Smartphonesalsosup
