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Related Experiment Video

Updated: Jul 9, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

Applications of machine learning algorithms to detect digital addiction: a meta-analysis.

Mengyang Xu1, Yandie Zheng2, Xingfa Long1

  • 1Quzhou College of Technology, Quzhou, China.

Frontiers in Psychiatry
|July 8, 2026
PubMed
Summary

Machine learning (ML) shows high accuracy in detecting digital addiction (DA), achieving 0.87 pooled classification accuracy. This automated approach offers a scalable solution for digital mental health screening.

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Area of Science:

  • Digital mental health
  • Computational psychiatry
  • Behavioral addiction research

Background:

  • Digital addiction (DA) is a growing global concern.
  • Traditional self-report methods for DA lack objectivity and consistency.
  • Machine learning (ML) presents a novel approach for automated DA detection.

Purpose of the Study:

  • To systematically evaluate the diagnostic accuracy of ML models for digital addiction.
  • To assess the performance of ML across different subtypes of digital addiction.
  • To compare the effectiveness of survey-based versus physiological data in ML-driven DA detection.

Main Methods:

  • A systematic meta-analysis of 64 studies (75 datasets; N=165,624) was performed.
  • Single-group proportion and bivariate diagnostic test accuracy (DTA) models were employed.
Keywords:
automated detectionclassification accuracydigital addictionmachine learningmeta-analysis

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  • Subgroup analyses were conducted for different addiction subtypes and data types.

    • Main Results:

    • The pooled classification accuracy for ML-based DA detection was 0.87 (95% CI [0.85, 0.90]).
    • The DTA framework achieved an AUC of 0.92, with balanced sensitivity and specificity (0.86).
    • High accuracy was observed for internet (0.90) and social media addiction (0.86); physiological data showed superior specificity (0.90).

    Conclusions:

    • ML-driven tools demonstrate significant potential as scalable screening instruments for digital addiction.
    • Further research requires representative sampling and standardized diagnostic criteria for advancing digital mental health.
    • Automated detection can overcome limitations of subjective self-report measures in diagnosing DA.