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Smartphone dependence classification using tensor factorization.

Jingyun Choi1, Mi Jung Rho2, Yejin Kim3

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Summary
This summary is machine-generated.

This study identified six smartphone usage patterns linked to smartphone dependence. These patterns, derived from user data, effectively predict addiction and can guide interventions for excessive smartphone use.

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Area of Science:

  • Behavioral Science
  • Data Science
  • Psychology

Background:

  • Excessive smartphone use is linked to personal and social issues.
  • Understanding smartphone dependence requires analyzing user behavior patterns.
  • Data-driven approaches can help classify and predict smartphone addiction.

Purpose of the Study:

  • To identify specific smartphone usage patterns correlated with smartphone dependence.
  • To develop a data-driven prediction algorithm for classifying smartphone dependence.
  • To establish intervention guidelines based on usage data analysis.

Main Methods:

  • Developed a mobile application to collect smartphone usage data from 48 users (41,683 logs).
  • Classified participants into control (SUC) and addiction (SUD) groups using the Korean Smartphone Addiction Proneness Scale for Adults (S-Scale) and clinical interviews.
  • Utilized tensor factorization to derive six optimal smartphone usage patterns.

Main Results:

  • Identified six key usage patterns: daytime social networking services (SNS), web surfing, nighttime SNS, mobile shopping, entertainment, and nighttime gaming.
  • Membership vectors derived from these patterns showed significantly better prediction performance than raw usage data.
  • Smartphone addiction group (SUD) users exhibited significantly longer usage times across all identified patterns compared to the control group (SUC).

Conclusions:

  • Smartphone usage patterns and their membership vectors are effective for assessing and predicting smartphone dependence.
  • These findings provide a basis for developing intervention guidelines to predict and treat smartphone dependence using objective usage data.
  • The data-driven classification approach offers a promising method for understanding and addressing problematic smartphone use.