Naturally occurring language as a source of evidence in suicide prevention.

@article{Resnik2020NaturallyOL,
  title={Naturally occurring language as a source of evidence in suicide prevention.},
  author={Philip Resnik and April Foreman and Michelle Kuchuk and Katherine Musacchio Schafer and Beau Pinkham},
  journal={Suicide \& life-threatening behavior},
  year={2020}
}
We discuss computational language analysis as it pertains to suicide prevention research, with an emphasis on providing non-technologists with an understanding of key issues and, equally important, considering its relation to the broader enterprise of suicide prevention. Our emphasis here is on naturally occurring language in social media, motivated by its non-intrusive ability to yield high-value information that in the past has been largely unavailable to clinicians. 
The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention
Suicide, a leading cause of death, is a complex and a hard-to-predict human tragedy. In this article, we introduce a comprehensive outlook on the emerging movement to integrate computational
The ethical role of computational linguistics in digital psychological formulation and suicide prevention.
Formulation is central to clinical practice. Formulation has a factor weighing, pattern recognition and explanatory hypothesis modelling focus. Formulation attempts to make sense of why a person
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task
TLDR
This is the first attempt to address the problem in mental health by conducting a shared task using sensitive data in a secure data enclave, and discusses the task, team results, and lessons learned to set the stage for future tasks on sensitive or confidential data.
A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis
TLDR
Among theories of suicide, theories within the Ideation-to-Action framework provided the most accurate prediction of suicide-related outcomes, when compared to theoretically-driven models, machine learning models provided superior prediction of ideation, attempts, and death.
Toward Suicidal Ideation Detection with Lexical Network Features and Machine Learning
TLDR
Results reveal that lexical networks are promising for classification and feature extraction as successful as the deep learning model and that a logistic classifier’s performance was comparable with thedeep learning method while promising explainability.
Can language use in social media help in the treatment of severe mental illness?
TLDR
Technological tools cannot, and should not, replace the all-important physician-patient relationship, but they may help physicians utilize all the available data to provide the best possible care.
Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”
TLDR
This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, which aims to provide real-time information about the language and its applications in the rapidly changing environment.
Capturing Changes in Mood Over Time in Longitudinal Data Using Ensemble Methodologies
TLDR
The system description of team BLUE for Task A of the CLPsych 2022 Shared Task on identifying changes in mood and behaviour in longitudinal textual data is presented, with an ensemble of Support Vector Machine, Logistic Regression, and Adaptive Boosting classifiers using emotion-informed embeddings as input representation.
Constructing a Psychometric Testbed for Fair Natural Language Processing
TLDR
The efforts to construct a corpus for psychometric natural language processing related to important dimensions such as trust, anxiety, numeracy, and literacy, in the health domain are described and preliminary results on use of the text to predict/categorize users’ survey response labels are reported.
...
...

References

SHOWING 1-10 OF 79 REFERENCES
A Prioritization Model for Suicidality Risk Assessment
TLDR
A well founded evaluation paradigm is introduced, and it is demonstrated using an expert-annotated test collection that meaningful improvements over plausible cascade model baselines can be achieved using an approach that jointly ranks individuals and their social media posts.
CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts
The shared task for the 2019 Workshop on Computational Linguistics and Clinical Psychology (CLPsych’19) introduced an assessment of suicide risk based on social media postings, using data from Reddit
Natural Language Processing of Social Media as Screening for Suicide Risk
TLDR
The feasibility of using social media data to detect those at risk for suicide, using natural language processing and machine learning techniques to detect quantifiable signals around suicide attempts, and designs for an automated system for estimating suicide risk are described.
Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing
TLDR
Good performance of the two classifiers in the evaluation study suggest they can be used to accurately detect mentions of suicide ideation and attempt within free-text documents in this psychiatric database.
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
TLDR
This paper develops a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation, and utilizes semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts.
Evaluation of Veterans' Suicide Risk With the Use of Linguistic Detection Methods.
TLDR
Analysis of clinical notes for outpatients who died from suicide and those who did not revealed a significant difference in clinicians' distancing language, which appears to be a marker of suicide risk.
Machine learning in suicide science: Applications and ethics.
TLDR
Overall, machine learning approaches hold promise for accurate, scalable, and effective suicide risk detection; however, many critical questions and issues remain unexplored.
Natural language processing in mental health applications using non-clinical texts†
TLDR
The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.
Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings
TLDR
Evaluation of risk-level annotations by experts yields what is, to the authors' knowledge, the first demonstration of reliability in risk assessment by clinicians based on social media postings.
Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid
TLDR
NLP-based models were able to generate relatively high predictive values based solely on responses to a simple general mood question and could provide a low-cost screening alternative in settings where lengthy structured item surveys are not feasible.
...
...