ArticleHealth information science and systems2023
MHA: a multimodal hierarchical attention model for depression detection in social media.
Article in Health information science and systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Depression detection from multimodalities based on LeNet with hunter-geese optimization.Scientific reports · 2026Article
- Early detection of mental health on social media using a hybrid Bi-LSTM-XGBoost model: a comparative study.Scientific reports · 2026Article
- Unseen struggles: A qualitative exploration of mental health challenges among higher education students in Tamil Nadu, India.Journal of education and health promotion · 2025Article
- DPD (DePression Detection) Net: a deep neural network for multimodal depression detection.Health information science and systems · 2024Article
- Explainable depression symptom detection in social media.Health information science and systems · 2024Review
- A systematic review on automated clinical depression diagnosis.Npj mental health research · 2023Review
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Authors and funding
6 authors.
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Abstract
As a serious mental disease, depression causes great harm to the physical and mental health of individuals, and becomes an important cause of suicide. Therefore, it is necessary to accurately identify and treat depressed patients. Compared with traditional clinical diagnosis methods, a large amount of real and different types of data on social media provides new ideas for depression detection research. In this paper, we construct a depression detection data set based on Weibo, and propose a Multimodal Hierarchical Attention (MHA) model for social media depression detection. Multimodal data is fed into the model and the attention mechanism is applied within and between modalities at the same time. Experimental results show that the proposed model achieves the best classification performance. In addition, we propose a distribution normalization method, which can optimize the data distribution and improve the accuracy of depression detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.