Depression and Self-Harm Risk Assessment in Online Forums

By Andrew Yates, Arman Cohan, Nazli Goharian. 2017

350 citations recorded by the source corpus.

Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.

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

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Glen A. Coppersmith

Citations in the corpus

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “For example, Yates et al. (2017) scale the risk of self-harm in posts about depression from green (indicating no risk) to critical.”
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems (2022, background): “For example, Yates et al. (2017) scale the risk of self-harm in posts about depression from green (indicating no risk) to critical.”
Natural language processing applied to mental illness detection: a narrative review (2022, background): “Yates et al. established a depression dataset named “ Reddit Self-reported Depression Diagnosis" (RSDD) 24 , which contains about 9k depressed users and 100k control users.”
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task (2021, methodology): “…data has led to a proliferation of proxy variables taken from publicly available data rather than ground-truth clinical variables or real-world outcomes (e.g. De Choudhury and De, 2014; Coppersmith et al., 2014; Yates et al., 2017; Shing et al., 2018; Cohan et al., 2018; Thorstad and Wolff, 2019).”
Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study (2020, background): “Machine learning models have been created to classify and characterize Reddit posts originating from mental health subreddits [7-10].”
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications (2019, background): “Another dataset is the Reddit Self-reported Depression Diagnosis (RSDD) dataset [90], which contains 9,000 diagnosed users with depression and approximately 107,000 matched control users.”