Natural language processing in mental health applications using non-clinical texts†

By Rafael A. Calvo, David N. Milne, M. Hussain, Helen Christensen. Natural Language Engineering, 2017

306 citations recorded by the source corpus.

Natural language processing (NLP) techniques can be used to make inferences about peoples' mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. 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.

Subfields: Applied Psychology, Artificial Intelligence, Social Psychology

View Paper: https://www.semanticscholar.org/paper/f83da7ed19b14b7272ca40e27ad76ecf9462262f

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Citations in the corpus

Detection of Depression-Related Posts in Reddit Social Media Forum (2019, unknown): “[16] who reviewed the taxonomy of data sources, NLP techniques and computational methods to detect various mental health applications.”
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare (2021, methodology): “, identifying emotions, mood, and profiles that might indicate mental health problems (Calvo et al., 2017).”
AI in mental health. (2020, background): “However, Internet technologies such as social media, online forums and instant messaging offer rich new sources of non-clinical text for such analysis [21].”
Artificial Intelligence for Mental Health and Mental Illnesses: An Overview (2019, background): “Mental health practice will rely heavily on NLP, prior to being able to perform other AI techniques, due to considerable raw input data in the form of text (e.g., clinical notes; other written language) and conversation (e.g., counseling sessions) [48, 51].”
Methods in predictive techniques for mental health status on social media: a critical review (2020, unknown): “● Random Selection of Control Users (23).”
Natural language processing applied to mental illness detection: a narrative review (2022, background): “Existing reviews introduce mainly the computational methods for mental health illness detection, they mostly focus on speci ﬁ c mental illnesses (suicide 7 – 9 , depression 10 – 12 ), or speci ﬁ c data sources (social media 13 – 15 , non-clinical texts 16 ).”
Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study (2020, background): “Using natural language processing (NLP) to infer mental states of individuals from their social media posts is a growing field (for reviews, see [5,6]).”