@inproceedings{c5c52419a6954dbe94ceaa866f4bdce6,
title = "Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses",
abstract = "Self-disclosed mental health diagnoses, which serve as ground truth annotations of mental health status in the absence of clinical measures, underpin the conclusions behind most computational studies of mental health language from the last decade. However, psychiatric conditions are dynamic; a prior depression diagnosis may no longer be indicative of an individual{\textquoteright}s mental health, either due to treatment or other mitigating factors. We ask: to what extent are self-disclosures of mental health diagnoses actually relevant over time? We analyze recent activity from individuals who disclosed a depression diagnosis on social media over five years ago and, in turn, acquire a new understanding of how presentations of mental health status on social media manifest longitudinally. We also provide expanded evidence for the presence of personality-related biases in datasets curated using self-disclosed diagnoses. Our findings motivate three practical recommendations for improving mental health datasets curated using self-disclosed diagnoses: 1. Annotate diagnosis dates and psychiatric comorbidities 2. Sample control groups using propensity score matching 3.",
author = "Keith Harrigian and Mark Dredze",
note = "Funding Information: We thank Ayah Zirikly and Carlos Aguirre for contributing annotations to use for evaluating inter-rater reliability. We also thank the anonymous reviewers for providing additional clinical grounding of our study and highlighting opportunities to improve our technical approach. Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 8th Workshop on Computational Linguistics and Clinical Psychology, CLPsych 2022 ; Conference date: 15-07-2022",
year = "2022",
language = "English (US)",
series = "CLPsych 2022 - 8th Workshop on Computational Linguistics and Clinical Psychology, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "59--75",
editor = "Ayah Zirikly and Dana Atzil-Slonim and Maria Liakata and Steven Bedrick and Bart Desmet and Molly Ireland and Andrew Lee and Sean MacAvaney and Matthew Purver and Rebecca Resnik and Andrew Yates",
booktitle = "CLPsych 2022 - 8th Workshop on Computational Linguistics and Clinical Psychology, Proceedings",
address = "United States",
}