# RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses

arXiv: https://arxiv.org/abs/1806.07916 · HTML: https://mlsanity.com/paper/1806.07916/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2018.

## Built on by (most cited first, 9 of 9)

- [Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting](https://mlsanity.com/paper/2310.07146/) (2023)
- [MentSum: A Resource for Exploring Summarization of Mental Health Online Posts](https://mlsanity.com/paper/2206.00856/) (2022)
- [Explainable Depression Symptom Detection in Social Media](https://mlsanity.com/paper/2310.13664/) (2023)
- [Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses](https://mlsanity.com/paper/2206.11155/) (2022)
- [Curriculum-guided Abstractive Summarization for Mental Health Online Posts](https://mlsanity.com/paper/2302.00954/) (2023)
- [Deep Temporal Modelling of Clinical Depression through Social Media Text](https://mlsanity.com/paper/2211.07717/) (2022)
- [MindTailor: Personalized Emotional Support via Post History-Grounded Case Formulation and Collaborative Refinement](https://mlsanity.com/paper/2606.21930/) (2026)
- [RSPC: A Benchmark for Modeling Stress and Psychiatric Conditions in Digitally Mediated Relationships using Psychiatrist Annotations](https://mlsanity.com/paper/2606.27247/) (2026)
- [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-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one's mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging.