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Updated: Jun 13, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Investigating the Fidelity of Digital Peer Support: A Preliminary Approach using Natural Language Processing to Scale

Arya Kadakia1, Sarah Masud Preum2, Andrew R Bohm3,4

  • 1BRiTE Center, Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, U.S.A.

Biomedical Engineering Systems and Technologies, International Joint Conference, BIOSTEC ... Revised Selected Papers. BIOSTEC (Conference)
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Summary

Digital peer support uses technology to help adults with serious mental illness manage chronic conditions. This study uses natural language processing to identify effective peer support techniques, aiming for 70% accuracy in classifying high-fidelity interventions.

Keywords:
Digital Peer SupportFidelityNLPNatural Language ProcessingPeer Support

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Area of Science:

  • Digital Health
  • Mental Health Services Research
  • Natural Language Processing

Background:

  • Adults with serious mental illness (SMI) face higher rates of chronic health conditions due to complex medical and psychiatric needs.
  • Peer specialists offer valuable support for illness management and community integration in individuals with lived experience.
  • Digital platforms are increasingly used for peer support, but scalable methods for monitoring service quality are lacking.

Purpose of the Study:

  • To analyze components of digital peer support interactions.
  • To develop a natural language processing (NLP) model for classifying evidence-based techniques used by peer support specialists.
  • To create a novel dataset for evaluating digital peer support fidelity.

Main Methods:

  • Development of a digital peer support corpus.
  • Application of natural language processing (NLP) techniques for data analysis.
  • Training and evaluation of a binary classifier to identify high-fidelity techniques.

Main Results:

  • The study aims to develop a binary classifier capable of achieving 70% accuracy.
  • Successful classification of high-fidelity, evidence-based techniques within digital peer support interactions.
  • Establishment of a scalable method for monitoring peer support fidelity in digital environments.

Conclusions:

  • Natural language processing offers a viable approach to assess and ensure the quality of digital peer support.
  • This research paves the way for scalable, technology-driven improvements in mental health support.
  • Findings support the integration of NLP for enhanced fidelity monitoring in digital mental health interventions.