Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution
By Guangyao Shen, Jia Jia, Liqiang Nie, Fuli Feng, Cunjun Zhang, Tianrui Hu, Tat-seng Chua, Wenwu Zhu. International Joint Conference on Artificial Intelligence, 2017
386 citations recorded by the source corpus.
Depression is a major contributor to the overall global burden of diseases. Traditionally, doctors diagnose depressed people face to face via referring to clinical depression criteria. However, more than 70% of the patients would not consult doctors at early stages of depression, which leads to further deterioration of their conditions. Meanwhile, people are increasingly relying on social media to disclose emotions and sharing their daily lives, thus social media have successfully been leveraged for helping detect physical and mental diseases. Inspired by these, our work aims to make timely depression detection via harvesting social media data. We construct well-labeled depression and non-depression dataset on Twitter, and extract six depression-related feature groups covering not only the clinical depression criteria, but also online behaviors on social media. With these feature groups, we propose a multimodal depressive dictionary learning model to detect the depressed users on Twitter. A series of experiments are conducted to validate this model, which outperforms (+3% to +10%) several baselines. Finally, we analyze a large-scale dataset on Twitter to reveal the underlying online behaviors between depressed and non-depressed users.
Subfields: Applied Psychology, Artificial Intelligence, Social Psychology
View Paper: https://www.semanticscholar.org/paper/da748238473d9cfbd3a6bda16566136242d7e6ba
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In Applied Psychology
In Artificial Intelligence
- Fotios Petropoulos
- Christoph Bergmeir
- Spyros Makridakis
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Devon K. Barrow
- Yanfei Kang
- Feng Li
In Social Psychology
Citations in the corpus
- Depression detection from social network data using machine learning techniques (2018, background): “The analysis of the emotional comments of social network data can be leveraged to produce reliable predicts in a variety of circumstances [25].”
- Forecasting: theory and practice (2020, background): “…preventative health care (Chen et al., 2015; van der Mark et al., 2014; Santos et al., 2015; Uematsu et al., 2014), to predict mental health issues (Shen et al., 2017; Tran et al., 2013), to assist diagnosis and disease progression (Ghassemi et al., 2015; Ma et al., 2017; Pierce et al., 2010; Qiao…”
- MAQuA: Adaptive Question-Asking for Multidimensional Mental Health Screening using Item Response Theory (2025, unknown): “…on single-task models, such * These authors contributed equally. as encoder-based classifiers for depression (Coppersmith et al. 2015; Eichstaedt et al. 2018), suicidal ideation (Shen et al. 2017) and anxiety (Owen, Camacho-Collados, and Es-pinosa Anke 2020; Gkotsis et al. 2017), detection.”
- MAQuA: Multi-outcome Adaptive Question-Asking for Mental Health using Item Response Theory (2026, unknown): “…classifiers for specific mental health and well-being dimensions such as depression (Coppersmith et al., 2015; Eichstaedt et al., 2018), suicidal ideation or risk (Shen et al., 2017; Varadara-jan et al., 2024a), resilience (Mahwish et al., 2026), anxiety (Owen et al., 2020; Juhng et al., 2023).”