Online expressions, offline struggles
Using social media to identify depression-related symptoms
Bibliographic Data
| ID | 22156981 |
|---|---|
| Authors | Mario Ezra Aragón (0000-0002-8213-957X, Universidade de Santiago de Compostela, corresponding author), Adrián Pastor López-Monroy (0000-0003-1018-4221, Universidad de Guanajuato, corresponding author), Manuel Montes-y-Gómez (0000-0002-7601-501X, National Institute of Astrophysics, Optics and Electronics), David E Losada (0000-0001-8823-7501, Universidade de Santiago de Compostela) |
| Year | 2025 |
| Volume | 50 |
| Pages | 100338 |
| Publication date | 2025-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Online Social Networks and Media (JOURNAL) |
| Journal identifiers | ISSN: 2468-6964 • E-ISSN: 2468-6964 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.osnem.2025.100338 |
| OpenAlex | W4415198788 |
| Language | EN |
| References cited | 24 |
With their growing popularity, social media platforms have become valuable tools for researchers and health professionals, offering new opportunities to identify linguistic patterns associated with mental health. In this study, we analyze depression-related symptoms using user-generated posts on social media and the Beck Depression Inventory (BDI). Using posts from individuals who have self-reported a depression diagnosis, we train and evaluate sentence classification models to assess their ability to detect BDI symptoms. Specifically, we conduct binary classification experiments to identify the presence of depression-related symptoms and additional tests to categorize sentences into specific BDI symptom types. We also perform a comprehensive symptom-level analysis to examine how depressive symptoms are expressed linguistically, linking social media data with a clinically validated framework. In addition, we analyze symptom distributions between users with and without depression and across platforms, providing insight into how symptoms manifest in diverse online contexts. Furthermore, we incorporate a data augmentation strategy that leverages Large Language Models to generate clinically grounded synthetic examples and evaluate their effectiveness against human-generated data. Our findings indicate that users with depression exhibit a significantly higher prevalence of certain BDI symptoms – particularly Suicidal Thoughts, Crying, Self-Dislike, and Changes in Sleeping Pattern – while control users predominantly express milder categories such as Sadness or Pessimism. Synthetic data improves the detection of underrepresented symptoms and enhances model robustness, although human-generated data better captures subtle linguistic nuances. Specialized models outperform general ones, but specific symptom categories remain challenging, underscoring the need for more interpretable and clinically grounded detection frameworks. • A thorough analysis using BDI to identify depression symptoms in social media posts. • A detailed study comparing synthetic and human data for depression symptom detection. • An exploration of how depression symptoms manifest across users and platforms
Categorization · Grounded theory · Mental health · Online and offline · Sadness · Sentence · Social media · Digital Mental Health Interventions · Mental Health via Writing · Sentiment Analysis and Opinion Mining
The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales
The CES-D Scale
Psychometric properties of the Beck Depression Inventory
Early depression detection in social media based on deep learning and underlying emotions
Multitask learning for recognizing stress and depression in social media
Advancing depression detection on social media platforms through fine-tuned large language models
An explainable ensemble model for revealing the level of depression in social media by considering personality traits and sentiment polarity pattern
Detecting mental disorder on social media
Leveraging Domain Knowledge to Improve Depression Detection on Chinese Social Media
What Does Your Bio Say? Inferring Twitter Users’ Depression Status From Multimodal Profile Information Using Deep Learning
Depression Intensity Estimation via Social Media
| Highly cited | No |
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