# Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses

arXiv: https://arxiv.org/abs/2206.11155 · HTML: https://mlsanity.com/paper/2206.11155/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 92 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 18 of 18)

- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Hypothesis Only Baselines in Natural Language Inference](https://mlsanity.com/paper/1805.01042/) (2018)
- [Adversarial Filters of Dataset Biases](https://mlsanity.com/paper/2002.04108/) (2020)
- [Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions](https://mlsanity.com/paper/2005.06676/) (2020)
- [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018)
- [Temporal Effects on Pre-trained Models for Language Processing Tasks](https://mlsanity.com/paper/2111.12790/) (2021)
- [Cross-validation failure: small sample sizes lead to large error bars](https://mlsanity.com/paper/1706.07581/) (2017)
- [PANDORA Talks: Personality and Demographics on Reddit](https://mlsanity.com/paper/2004.04460/) (2020)
- [On the State of Social Media Data for Mental Health Research](https://mlsanity.com/paper/2011.05233/) (2020)
- [Forecasting the onset and course of mental illness with Twitter data](https://mlsanity.com/paper/1608.07740/) (2016)
- [Multi-Task Learning for Mental Health using Social Media Text](https://mlsanity.com/paper/1712.03538/) (2017)
- [Gender and Racial Fairness in Depression Research using Social Media](https://mlsanity.com/paper/2103.10550/) (2021)
- [Quantifying Mental Health from Social Media with Neural User Embeddings](https://mlsanity.com/paper/1705.00335/) (2017)
- [Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health](https://mlsanity.com/paper/2109.13770/) (2021)
- [RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses](https://mlsanity.com/paper/1806.07916/) (2018)
- [Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation](https://mlsanity.com/paper/1807.07351/) (2018)
- [Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement](https://mlsanity.com/paper/2010.06472/) (2020)

## Built on by (most cited first, 4 of 4)

- [It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers](https://mlsanity.com/paper/2301.05453/) (2023)
- [Using LLMs to Aid Annotation and Collection of Clinically-Enriched Data in Bipolar Disorder and Schizophrenia](https://mlsanity.com/paper/2406.12687/) (2024)
- [Context is Important in Depressive Language: A Study of the Interaction Between the Sentiments and Linguistic Markers in Reddit Discussions](https://mlsanity.com/paper/2405.18061/) (2024)
- [WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection](https://mlsanity.com/paper/2607.04350/) (2026)

## Abstract (arXiv)

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 comorbidities; 2) Sample control groups using propensity score matching; 3) Identify and remove spurious correlations introduced by selection bias.