Citation

Li Z, An Z, Cheng W, Zhou J, Zheng F, Hu B. Health Inf. Sci. Syst. 2023; 11(1): e6.

Copyright

(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1007/s13755-022-00197-5

PMID

36660408

PMCID

PMC9846704

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.

Language: en

Keywords

Social media; Attention mechanism; Deep neural network; Depression detection; Multimodality