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Machine Learning-Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal

Zachary Kaminsky1,2,3,4, Robyn J McQuaid1,5, Kim Gc Hellemans5

  • 1University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.

Journal of Medical Internet Research
|December 5, 2024
PubMed
Summary
This summary is machine-generated.

The Suicide Artificial Intelligence Prediction Heuristic (SAIPH) algorithm shows promise in predicting suicidal ideation (SI) using social media data. SAIPH effectively tracks changes in SI over time and identifies factors influencing suicidal trajectories.

Keywords:
Twittermachine learningnatural language processingpredictionsocial mediasuicidal ideationsuicidal mentionsuicidesuicide risksuicide risk modelvalidation

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Area of Science:

  • Computational psychiatry
  • Social media analytics
  • Mental health technology

Background:

  • Machine learning and natural language processing show potential for predicting suicide risk from social media.
  • Previous research indicates the feasibility of using AI for mental health monitoring.

Purpose of the Study:

  • To externally validate the Suicide Artificial Intelligence Prediction Heuristic (SAIPH) algorithm using independent cohort survey data.
  • To assess SAIPH's efficacy in tracking changes in suicidal ideation (SI) over time.
  • To explore factors influencing suicidal trajectories on social media.

Main Methods:

  • Twitter (X) data from student and COVID-19 cohorts were scored using SAIPH.
  • SAIPH scores were compared against validated survey instruments for depression and SI.
  • Longitudinal analysis of SAIPH scores was conducted to evaluate SI trajectory changes.

Main Results:

  • SAIPH scores interacted with posting frequency to correlate with quantitative SI metrics in both cohorts.
  • The daily SAIPH score slope predicted changes in SI over short periods (≤2 weeks).
  • SAIPH identified a negative association between SAIPH score and time in individuals with suicidal mentions, particularly within 72 hours of response.

Conclusions:

  • SAIPH is validated as a measure associated with perceived stress, SI, and SI changes.
  • The study introduces novel methods for assessing social media's impact on suicidal trajectories.
  • SAIPH offers a tool for real-time monitoring and understanding of mental health trends.