Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution

Guangyao Shen · Jia Jia · Liqiang Nie · Fuli Feng · Cunjun Zhang · Tianrui Hu +2 more

401 citations0/0 files storedInternational Joint Conference on Artificial Intelligence

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Mental Health via Writing Sentiment Analysis and Opinion Mining Emotion and Mood Recognition

Abstract

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.

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References

2013Diagnostic and Statistical Manual of Mental Disorders 1977The CES-D Scale 1961An Inventory for Measuring Depression 2008Diagnostic and Statistical Manual of Mental Disorders(DSM)2021Predicting Depression via Social Media 1999Affective Norms for English Words (ANEW): Instruction manual and affective ratings 2014Quantifying Mental Health Signals in Twitter 2014Diagnostic and Statistical Manual of Mental Disorders

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2022RETRACTED: Deep Learning for Depression Detection from Textual Data 2021A textual-based featuring approach for depression detection using machine learning classifiers and social media texts 2022Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media 2020Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features 2022An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM

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