基于社交媒体中文本信息的早期抑郁症检测

doi:10.3969/j.issn.0258-8021.2022.01.003

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Abstract The traditional method of diagnosing depression is through face-to-face assessment and conversation. However, many patients with depression are reluctant to seek medical attention at an early stage, which makes their condition worse. In order to judge the situation of patients with depression in the early stage, a detection model using time series features of social media textual information and multi-instance learning was proposed in this work. Considering that depressive symptoms will not appear immediately, the use of time series samples will be very important. Therefore, the unsupervised LSTM was used to extract time series features, binary classification was implemented by training a classifier, and a multi-instance learning model was exploited to solve the problem of unbalanced samples. Naive Bayes classifiers, random forests, multivariate social network learning and multimodal depression dictionary learning were used as benchmark methods firstly, and then the multi-instance learning with unsupervised LSTM time series functions was employed to detect depression more accurately. On the basis of the MDDL dataset, 200 survey subjects totally 7946 tweets were selected, and the training-test ratio was set as 8:2. Experimental results were following: the accuracy, precision, recall and F1 score reached 75.0%, 76.0%, 73.0%, and 74.5%, respectively, which demonstrated that it was feasible to use machine learning for early depression detection through text data in social media. In addition, a large number of ablation studies also verified that the method using time series features could achieve better performance than the traditional benchmark methods.

Key words： depression detection long short-term memory (LSTM) time series feature social media multi-instance learning

Received: 08 March 2021

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R318

Corresponding Authors: ^* E-mail:zhanghf@njust.edu.cn

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	Articles by authors
	Zhang Mengna
	Wang Junyan
	Long Yang
	Zhang Haofeng
	Hu Yong

Cite this article:

Zhang Mengna,Wang Junyan,Long Yang, et al. Early Detection of Depression Based on Textual Information in Social Media[J]. Chinese Journal of Biomedical Engineering, 2022, 41(1): 21-30.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2022.01.003 OR http://cjbme.csbme.org/EN/Y2022/V41/I1/21

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