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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Analyzing Suicide Risk From Linguistic Features in Social Media: Evaluation Study.

Cecilia Lao1, Jo Lane2, Hanna Suominen1,3

  • 1School of Computing, College of Engineering and Computer Science, The Australian National University, Canberra, ACT, Australia.

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Summary

Machine learning analysis of social media language can identify suicide risk. Linguistic features like authenticity and negation are key indicators, aiding early intervention and prevention efforts.

Keywords:
evaluation studyinterdisciplinary researchlinguisticsmachine learningmental healthnatural language processingsocial mediasuicide risk

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Area of Science:

  • Computational linguistics
  • Machine learning
  • Mental health informatics

Background:

  • Effective suicide risk assessment is crucial for prevention.
  • Individuals at risk may not seek professional help.
  • Machine learning (ML) and computational linguistics offer tools for risk analysis.

Purpose of the Study:

  • To explore the use of computerized language analysis for assessing suicide risk on social media.
  • To understand suicide risk through statistical and ML-based text analysis.

Main Methods:

  • Analysis of the University of Maryland Suicidality Dataset (N=866 Reddit users).
  • Linguistic Inquiry and Word Count lexicon for sentiment, thinking styles, and part of speech; TextStat for readability.
  • Statistical tests (Mann-Whitney U, Kruskal-Wallis) and ML models (gradient boost, random forest, SVM) with 10-fold cross-validation.

Main Results:

  • Statistically significant linguistic differences between at-risk and no-risk users (P<.05).
  • At-risk users showed higher authenticity, first-person pronouns, and negation; lower clout.
  • Random forest and gradient boost models achieved higher F1-scores (0.65, 0.62) than SVM (0.52).

Conclusions:

  • Linguistic features like social posturing (authenticity, clout), pronoun use, and negation are associated with suicide risk.
  • This research enhances understanding of at-risk users' thought patterns and ML model mechanisms.
  • Demonstrated ML's potential to assist healthcare professionals in suicide risk assessment.