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Suicide Risk and Protective Factors in Online Support Forum Posts: Annotation Scheme Development and Validation Study
Stevie Chancellor1, Steven A Sumner2, Corinne David-Ferdon3
1Department of Computer Science & Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, United States.
JMIR Mental Health
|November 8, 2021
Summary
Researchers developed a reliable annotation scheme to identify suicide risk and protective factors in online crisis forum posts. This framework enhances machine learning models for suicide prevention and public health interventions.
Area of Science:
- Computational Social Science
- Public Health
- Psychiatry
Background:
- Online communities offer support for individuals experiencing suicidal ideation and crisis.
- Machine learning models increasingly use community data to predict suicide risk.
- Limited research exists on identifying both risk and protective factors within online posts.
Purpose of the Study:
- To develop a valid and reliable annotation scheme for evaluating risk and protective factors for suicidal ideation.
- To improve the assessment of suicide risk in online crisis forums.
Main Methods:
- Designed a clinically grounded process for identifying risk and protective markers in social media data.
- Drew on construct validity and measurement in social sciences.
- Applied the scheme to annotate 200 posts from the r/SuicideWatch Reddit community.
Main Results:
- Produced an annotation scheme consistent with public health coding schemes for suicide.
- Advanced attention to protective factors in suicide risk assessment.
- Demonstrated high internal validity and consistency with prior research findings.
Conclusions:
- Formalized a framework incorporating construct validity for social media suicide risk annotation schemes.
- Advanced understanding of risk and protective factors in social media data.
- Aids public health suicide prevention programming and computational social science research.
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