Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 40 references that are in the map. First posted on arXiv 28 Apr 2022.
Builds on
9 of 40 references.
References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2017Depression and Self-Harm Risk Assessment in Online Forumscited by 81
- 2019A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streamscited by 18
- 2021Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERTcited by 16
- 2021An Exploratory Analysis of the Relation Between Offensive Language and Mental Healthcited by 14
- 2018HappyDB: A Corpus of 100,000 Crowdsourced Happy Momentscited by 9
- 2020Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scorescited by 4
- 2021Deep Bag-of-Sub-Emotions for Depression Detection in Social Mediacited by 4
- 2021A Psychologically Informed Part-of-Speech Analysis of Depression in Social Mediacited by 4
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Citing papers per year: 2025: 1.
1 paper.