Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings
Han-Chin Shing, Suraj Nair, Ayah Zirikly, Meir Friedenberg, Hal Daumé III, Philip Resnik
Abstract
We report on the creation of a dataset for studying assessment of suicide risk via online postings in Reddit. Evaluation of risk-level annotations by experts yields what is, to our knowledge, the first demonstration of reliability in risk assessment by clinicians based on social media postings. We also introduce and demonstrate the value of a new, detailed rubric for assessing suicide risk, compare crowdsourced with expert performance, and present baseline predictive modeling experiments using the new dataset, which will be made available to researchers through the American Association of Suicidology.- Anthology ID:
- W18-0603
- Volume:
- Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, LA
- Editors:
- Kate Loveys, Kate Niederhoffer, Emily Prud’hommeaux, Rebecca Resnik, Philip Resnik
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 25–36
- Language:
- URL:
- https://aclanthology.org/W18-0603
- DOI:
- 10.18653/v1/W18-0603
- Cite (ACL):
- Han-Chin Shing, Suraj Nair, Ayah Zirikly, Meir Friedenberg, Hal Daumé III, and Philip Resnik. 2018. Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pages 25–36, New Orleans, LA. Association for Computational Linguistics.
- Cite (Informal):
- Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings (Shing et al., CLPsych 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W18-0603.pdf