# Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression

arXiv: https://arxiv.org/abs/2204.13569 · HTML: https://mlsanity.com/paper/2204.13569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams](https://mlsanity.com/paper/1905.08772/) (2019)
- [Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT](https://mlsanity.com/paper/2106.16175/) (2021)
- [An Exploratory Analysis of the Relation Between Offensive Language and Mental Health](https://mlsanity.com/paper/2105.14888/) (2021)
- [HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments](https://mlsanity.com/paper/1801.07746/) (2018)
- [Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores](https://mlsanity.com/paper/2011.01695/) (2020)
- [Deep Bag-of-Sub-Emotions for Depression Detection in Social Media](https://mlsanity.com/paper/2103.01334/) (2021)
- [A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media](https://mlsanity.com/paper/2108.00279/) (2021)

## Built on by (most cited first, 1 of 1)

- [Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice](https://mlsanity.com/paper/2501.00982/) (2025)

## Abstract (arXiv)

In this work, we explore the relationship between depression and manifestations of happiness in social media. While the majority of works surrounding depression focus on symptoms, psychological research shows that there is a strong link between seeking happiness and being diagnosed with depression. We make use of Positive-Unlabeled learning paradigm to automatically extract happy moments from social media posts of both controls and users diagnosed with depression, and qualitatively analyze them with linguistic tools such as LIWC and keyness information. We show that the life of depressed individuals is not always bleak, with positive events related to friends and family being more noteworthy to their lives compared to the more mundane happy events reported by control users.