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Cited 3 time in webofscience Cited 4 time in scopus
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Smartphone dependence classification using tensor factorization

Title
Smartphone dependence classification using tensor factorization
Authors
Jingyun ChoiMi Jung RhoYejin KimIn Hye YookHwanjo YuDai-Jin KimIn Young Choi
Date Issued
Jun-2017
Publisher
Public Library of Science
Abstract
Excessive smartphone use causes personal and social problems. To address this issue, we sought to derive usage patterns that were directly correlated with smartphone dependence based on usage data. This study attempted to classify smartphone dependence using a data-driven prediction algorithm. We developed a mobile application to collect smartphone usage data. A total of 41,683 logs of 48 smartphone users were collected from March 8, 2015, to January 8, 2016. The participants were classified into the control group (SUC) or the addiction group (SUD) using the Korean Smartphone Addiction Proneness Scale for Adults (S-Scale) and a face-to-face offline interview by a psychiatrist and a clinical psychologist (SUC = 23 and SUD = 25). We derived usage patterns using tensor factorization and found the following six optimal usage patterns: 1) social networking services (SNS) during daytime, 2) web surfing, 3) SNS at night, 4) mobile shopping, 5) entertainment, and 6) gaming at night. The membership vectors of the six patterns obtained a significantly better prediction performance than the raw data. For all patterns, the usage times of the SUD were much longer than those of the SUC. From our findings, we concluded that usage patterns and membership vectors were effective tools to assess and predict smartphone dependence and could provide an intervention guideline to predict and treat smartphone dependence based on usage data.
URI
http://oasis.postech.ac.kr/handle/2014.oak/39088
ISSN
1932-6203
Article Type
Article
Citation
PLoS ONE, vol. 12, no. 6, 2017-06
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 YU, HWANJO
Dept of Computer Science & Enginrg
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