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

By Philip Resnik, April Foreman, M. Kuchuk, Katherine Musacchio Schafer, B. Pinkham. 2020

35 citations recorded by the source corpus.

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.

Subfields: Applied Psychology, Artificial Intelligence, Social Psychology, Natural Language Processing, Medical & Health AI

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Citations in the corpus

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “Despite the potential for NLP in this area, there are serious ethical implications (Ophir et al., 2021; Resnik et al., 2021).”
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems (2022, background): “Despite the potential for NLP in this area, there are a number of serious ethical implications (Ophir et al., 2021; Resnik et al., 2021).”
The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention (2021, methodology): “Thus, to maintain participants’ right to privacy, researchers are advised to adhere to consensual ethical norms, obtain informed consents from potential participants, and allow them to opt out whenever they choose to (Resnik et al., 2021; Verma, 2014).”
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task (2021, background): “Resnik et al. (2021) offer an overview of issues more speciﬁcally focused on using naturally occurring language as a source of evidence in suicide prediction.”