@inproceedings{harrigian-dredze-2022-now,
title = "Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses",
author = "Harrigian, Keith and
Dredze, Mark",
editor = "Zirikly, Ayah and
Atzil-Slonim, Dana and
Liakata, Maria and
Bedrick, Steven and
Desmet, Bart and
Ireland, Molly and
Lee, Andrew and
MacAvaney, Sean and
Purver, Matthew and
Resnik, Rebecca and
Yates, Andrew",
booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
month = jul,
year = "2022",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.clpsych-1.6",
doi = "10.18653/v1/2022.clpsych-1.6",
pages = "59--75",
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{'}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 comorbidities2) Sample control groups using propensity score matching3) Identify and remove spurious correlations introduced by selection bias",
}
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%0 Conference Proceedings
%T Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses
%A Harrigian, Keith
%A Dredze, Mark
%Y Zirikly, Ayah
%Y Atzil-Slonim, Dana
%Y Liakata, Maria
%Y Bedrick, Steven
%Y Desmet, Bart
%Y Ireland, Molly
%Y Lee, Andrew
%Y MacAvaney, Sean
%Y Purver, Matthew
%Y Resnik, Rebecca
%Y Yates, Andrew
%S Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F harrigian-dredze-2022-now
%X 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’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 comorbidities2) Sample control groups using propensity score matching3) Identify and remove spurious correlations introduced by selection bias
%R 10.18653/v1/2022.clpsych-1.6
%U https://aclanthology.org/2022.clpsych-1.6
%U https://doi.org/10.18653/v1/2022.clpsych-1.6
%P 59-75
Markdown (Informal)
[Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses](https://aclanthology.org/2022.clpsych-1.6) (Harrigian & Dredze, CLPsych 2022)
ACL