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Cited by 506Mental Health via WritingSuicide and Self-Harm StudiesMachine Learning in Healthcare

Natural language processing of social media as screening for suicide risk

Open access
Coppersmith, Glen, Leary, Ryan, Crutchley, Patrick, Fine, Alex·2018·Biomedical Informatics Insights
DOI 10.1177/1178222618792860PMC PMC6111391
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Abstract

Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people’s lives are meaningfully affected, and almost any other statistic around suicide deaths is equally alarming. The pervasiveness of social media—and the near-ubiquity of mobile devices used to access social media networks—offers new types of data for understanding the behavior of those who (attempt to) take their own lives and suggests new possibilities for preventive intervention. We demonstrate the feasibility of using social media data to detect those at risk for suicide. Specifically, we use natural language processing and machine learning (specifically deep learning) techniques to detect quantifiable signals around suicide attempts, and describe designs for an automated system for estimating suicide risk, usable by those without specialized mental health training (eg, a primary care doctor). We also discuss the ethical use of such technology and examine privacy implications. Currently, this technology is only used for intervention for individuals who have “opted in” for the analysis and intervention, but the technology enables scalable screening for suicide risk, potentially identifying many people who are at risk preventively and prior to any engagement with a health care system. This raises a significant cultural question about the trade-off between privacy and prevention—we have potentially life-saving technology that is currently reaching only a fraction of the possible people at risk because of respect for their privacy. Is the current trade-off between privacy and prevention the right one?

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  • One study used natural language processing and deep learning to estimate suicide risk based on patterns in social media activity, but the authors noted that their goal was to find patterns indicating risk over a longer period instead of short-term state changes.

    “One study used natural language processing and deep learning to estimate suicide risk based on patterns in social media activity, but the authors noted that their goal was to find patterns indicating risk over a longer period instead of short-term state changes (45).”

    From New methods for assessing rapid changes in suicide risk (2021) · cites this paper as [45]

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Flagged first, then by citations

Research works printed but not linked: 24

  1. [15]Coppersmith G Hilland C Frieder O Leary R. Scalable mental health analysis in the clinical whitespace via natural language processing. Paper presented at: 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI); February 16-19, 2017; Orlando, FL: 393– 396. New York, NY: IEEE.
  2. [17]Chung C Pennebaker J. The psychological functions of function words. In: Fiedler K, ed. Frontiers of Social Psychology: Social Communication. New York, NY: Psychology Press, 2007: 343– 359.
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  4. [19]Coppersmith G Harman C Dredze M. Measuring post traumatic stress disorder in Twitter. Paper presented at: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (ICWSM); June 1–4, 2014; Ann Arbor, MI.
  5. [20]Cohan A Young S Goharian N. Triaging mental health forum posts. Paper presented at: Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (Vol. 16); June 16, 2016; San Diego, CA.
  6. [21]Coppersmith G Dredze M Harman C Hollingshead K Mitchell M. CLPsych 2015 shared task: depression and PTSD on Twitter. Paper presented at: Proceedings of the Shared Task for the NAACL Workshop on Computational Linguistics and Clinical Psychology; June 5, 2015; Denver, CO.
  7. [22]Resnik P Armstrong W Claudino L Nguyen T Nguyen VA Boyd-Graber J. The University of Maryland CLPsych 2015 shared task system. Paper presented at: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (North American Chapter of the Association for Computational Linguistics); June 5, 2015; Denver, CO.
  8. [23]Preotiuc-Pietro D Sap M Schwartz HAS Ungar L. Mental illness detection at the World Well-Being Project for the CLPsych 2015 shared task. Paper presented at: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (North American Chapter of the Association for Computational Linguistics); June 5, 2015; Denver, CO.
  9. [24]Pedersen T. Screening Twitter users for depression and PTSD with lexical decision lists. Paper presented at: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (North American Chapter of the Association for Computational Linguistics); June 5, 2015; Denver, CO.
  10. [26]Mitchell M Hollingshead K Coppersmith G. Quantifying the language of schizophrenia in social media. Paper presented at: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (North American Chapter of the Association for Computational Linguistics); June 5, 2015; Denver, CO.
  11. [29]Coppersmith G Dredze M Harman C. Quantifying mental health signals in Twitter. Paper presented at: Proceedings of the ACL Workshop on Computational Linguistics and Clinical Psychology; June 27, 2014; Baltimore, MD.
  12. [30]Coppersmith G Leary R Whyne E Wood T Quantifying suicidal ideation via language usage on social media. Paper presented at: Joint Statistics Meetings Proceedings, Statistical Computing Section (JSM); August 2015; Seattle, WA.
  13. [36]Coppersmith G Ngo K Leary R Wood T. Exploratory data analysis of social media prior to a suicide attempt. Paper presented at: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (North American Chapter of the Association for Computational Linguistics); June 16, 2016; San Diego, CA.
  14. [37]Dinakar K Chen J Lieberman H Picard R Filbin R. Mixed-initiative real-time topic modeling & visualization for crisis counseling. Paper presented at: Proceedings of the 20th International Conference on Intelligent User Interfaces; March 29-April 1, 2015; Atlanta, GA: 417– 426. New York, NY: ACM.
  15. [39]Coppersmith G Dredze M Harman C Hollingshead K. From ADHD to SAD: analyzing the language of mental health on Twitter through self-reported diagnoses. Paper presented at: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (North American Chapter of the Association for Computational Linguistics); June 5, 2015; Denver, CO.
  16. [40]Loveys K Crutchley P Wyatt E Coppersmith G. Small but mighty: affective micropatterns for quantifying mental health from social media language. Paper presented at: Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology—From Linguistic Signal to Clinical Reality; August 3, 2017; Vancouver, BC, Canada: 85– 95. New York, NY: ACL.
  17. [41]Benton A Coppersmith G Dredze M. Ethical research protocols for social media health research. Paper presented at: Proceedings of the First Workshop on Ethics in Natural Language Processing; April 4, 2017; Valencia, Spain: 94– 102. New York, NY: ACL.
  18. [42]Sap M Park G Eichstaedt JC et al Developing age and gender predictive lexica over social media. Paper presented at: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP); October 25–29, 2014; Doha, Qatar: 1146– 1151. New York, NY: ACL.
  19. [45]Yang Z Yang D Dyer C He X Smola AJ Hovy EH. Hierarchical attention networks for document classification. Paper presented at: HLT-NAACL; June 12–17, 2016; San Diego, CA.
  20. [46]Pennington J Socher R Manning CD. GloVe: global vectors for word representation. Paper presented at: Empirical Methods in Natural Language Processing (EMNLP); October 25–29, 2014; Doha, Qatar: 1532– 1543. New York, NY: ACL.
  21. [48]Xu K Ba J Kiros R et al Show, attend and tell: neural image caption generation with visual attention. Paper presented at: International Conference on Machine Learning; July 6-11, 2015; Lille, France: 2048– 2057.
  22. [53]De Choudhury M Sharma SS Logar T Eekhout W Nielsen RC Gender and cross-cultural differences in social media disclosures of mental illness. Paper presented at: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17); February 25-March 1, 2017; Portland, OR: 353– 369. New York, NY: ACM.
  23. [56]Facebook. Getting our community help in real time (press release), 2017 https://newsroom.fb.com/news/2017/11/getting-our-community-help-in-real-time/.
  24. [58]Parker J Wei Y Yates A Frieder O Goharian N. A framework for detecting public health trends with Twitter. Paper presented at: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM ’13); August 25–28, 2013; Niagara, ON, Canada: 556– 563. New York, NY: ACM.

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Authors
Coppersmith, G., Leary, R., Crutchley, P., & Fine, A.
Affiliations
Qntfy, Boston, MA, USA
Corresponding author
Coppersmith, Glen
ORCID
Venue
Biomedical Informatics Insights
Publisher
SAGE Publications
Published
2018-01
Volume
10
Pages
1178222618792860-1178222618792860
ISSN
1178-2226 (ISSN-L)
DOI
10.1177/1178222618792860
PMC
PMC6111391
OpenAlex
W2889391310
Cited by
506
References
32
Citations by year
2026: 45 · 2025: 72 · 2024: 56 · 2023: 61 · 2022: 73 · 2021: 116 · 2020: 62 · 2019: 18 · 2017: 1
FWCI
7.44
Type
journal article
Language
en
Open access
Licence
Locations
  • Biomedical Informatics Insights (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
  • Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
suicide risk screening, suicide attempts, social media, natural language processing, primary care, privacy, ethics, mental health, preventive interventions

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