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The Workshop on Computational Linguistics and Clinical Psychology

CLPsych, a workshop series founded in 2014

CLPsych 2022 will be held in conjunction with NAACL 2022

July 15th 2022

The Eighth Workshop on Computational Linguistics and Clinical Psychology: Mental Health in the Face of Change

A hybrid workshop to be held in conjunction with NAACL 2022. Further details will be available soon.

Since 2014, CLPsych has brought together researchers in computational linguistics and NLP, who use computational methods to better understand human language, infer meaning and intention, and predict individuals’ characteristics and potential behavior, with mental health practitioners and researchers, who are focused on psychopathology and neurological health and engage directly with the needs of providers and their patients. This workshop’s distinctly interdisciplinary nature has improved the exchange of knowledge, fostered collaboration, and increased the visibility of mental health as a problem domain in NLP.

The continued pressure resulting from the COVID-19 pandemic, with many people experiencing long-term financial, health, and social stressors, has globally exacerbated threats to mental health that experts have expressed serious concerns about. CLPsych has an important role to play in bringing people together to discuss and exchange their recent work and results. Together, we hope to be able to advance the common goal of using human language as a tool to better understand emotional and mental state, and reducing emotional suffering and the potential for self-harm. 

This year we are adopting a theme around mental health in the face of change. This ties in with our shared task with making longitudinal predictions, but also with the kind of aspects that technology would need to have (e.g. explainability and fairness) to be integrated into clinical practice. We are also highly interested in papers that seek to understand people who are difficult to reach, who are traditionally less likely to seek and receive help, or who may be socially or digitally excluded because of conventional measurement/diagnosis or care models.

Given the uncertainties associated with the pandemic, we are planning for a hybrid workshop that will permit in-person participation if the physical conference takes place as planned, while also permitting remote participation. This will not only expand the reach of the workshop to people who might not typically attend in person, but it will also enable a smooth transition to a fully virtual workshop if that proves necessary. 

Organizing Committee

(In alphabetical order)


Bar-Ilan University


Ayah Zirikly

Johns Hopkins University (co-chair)

Steven Bedrick

Oregon Health & Science University

National Institutes of Health

Molly Ireland


Andrew Lee

University of Michigan

Sean MacAvaney

University of Glasgow

Matthew Purver

Queen Mary University of London

Rebecca Resnik

Rebecca Resnik and Associates

Andrew Yates

University of Amsterdam


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Yaara Shriki and colleagues in “Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis” fine-tune a language model and mask nouns, verbs and some other morphosyntactic categories to detect transcribed speech of patients diagnosed with schizophrenia

Daeun Lee et al. developed the suicidality detection model C-GraphSAGE based on a graph neural network to grasp the dynamic semantic information of social media post by using the suicide vocabulary efficiently.

Check out shared task paper “Multi-Task Learning to Capture Changes in Mood Over Time”. The authors investigate the impact of using MTL to predict mood changes over time of social media users –From: uOttawa-AI

To what extent do people who engage in non-suicidal self-injury conceptualize their behavior as an addiction? Using #reddit data we identify shared language across #NSSI and #SubstanceUse Reddits, showing borrowed #recovery language when describing self-harm

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