Assessing depression risk in Chinese microblogs: A corpus and machine learning methods

Xiaofeng Wang; Shuai Chen; Tao Li; Wanting Li; Yejie Zhou; Jie Zheng; Yaoyun Zhang; Buzhou Tang

doi:10.1109/ICHI.2019.8904506

Assessing depression risk in Chinese microblogs: A corpus and machine learning methods

Xiaofeng Wang
, Shuai Chen
, Tao Li
, Wanting Li
, Yejie Zhou
, Jie Zheng
, Yaoyun Zhang
, Buzhou Tang^*

^*Corresponding author for this work

Shenzhen University
Harbin Institute of Technology Shenzhen
DC

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

20 Scopus citations

Abstract

Depression is a serious challenge for personal and public health. Tens of millions of people suffer from depression every year, but only a small part of them can receive professional treatment. Because people usually avoid disclosing or discussing mental health conditions, which are considered as stigma or taboo. Social network, such as the most popular Chinese microblogging website Sina Weibo, provides a channel for people, including depressed patients, to share their thoughts. We may find depressed emotions from microblogs. In this paper, we randomly collected a set of microblogs and manually annotated them with depression risk at level 0-3 (from mild to severe). On this corpus, we compared different machine learning methods for depression risk prediction, and provided benchmark results. The machine learning methods are support vector machine (SVM), convolutional neural network (CNN), long short-term memory network (LSTM) and bidirectional encoder representations from transformers (BERT).

Original language	English
Title of host publication	2019 IEEE International Conference on Healthcare Informatics, ICHI 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538691380
DOIs	https://doi.org/10.1109/ICHI.2019.8904506
State	Published - Jun 2019
Externally published	Yes
Event	7th IEEE International Conference on Healthcare Informatics, ICHI 2019 - Xi'an, China Duration: 10 Jun 2019 → 13 Jun 2019

Publication series

Name	2019 IEEE International Conference on Healthcare Informatics, ICHI 2019

Conference

Conference	7th IEEE International Conference on Healthcare Informatics, ICHI 2019
Country/Territory	China
City	Xi'an
Period	10/06/19 → 13/06/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Assessing depression risk
Chinese microblogs
Machine learning

Access to Document

10.1109/ICHI.2019.8904506

Cite this

Wang, X., Chen, S., Li, T., Li, W., Zhou, Y., Zheng, J., Zhang, Y., & Tang, B. (2019). Assessing depression risk in Chinese microblogs: A corpus and machine learning methods. In 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019 Article 8904506 (2019 IEEE International Conference on Healthcare Informatics, ICHI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2019.8904506

@inproceedings{ef1d95f71fa94ce2a5689a3732eaa737,

title = "Assessing depression risk in Chinese microblogs: A corpus and machine learning methods",

abstract = "Depression is a serious challenge for personal and public health. Tens of millions of people suffer from depression every year, but only a small part of them can receive professional treatment. Because people usually avoid disclosing or discussing mental health conditions, which are considered as stigma or taboo. Social network, such as the most popular Chinese microblogging website Sina Weibo, provides a channel for people, including depressed patients, to share their thoughts. We may find depressed emotions from microblogs. In this paper, we randomly collected a set of microblogs and manually annotated them with depression risk at level 0-3 (from mild to severe). On this corpus, we compared different machine learning methods for depression risk prediction, and provided benchmark results. The machine learning methods are support vector machine (SVM), convolutional neural network (CNN), long short-term memory network (LSTM) and bidirectional encoder representations from transformers (BERT).",

keywords = "Assessing depression risk, Chinese microblogs, Machine learning",

author = "Xiaofeng Wang and Shuai Chen and Tao Li and Wanting Li and Yejie Zhou and Jie Zheng and Yaoyun Zhang and Buzhou Tang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 7th IEEE International Conference on Healthcare Informatics, ICHI 2019 ; Conference date: 10-06-2019 Through 13-06-2019",

year = "2019",

month = jun,

doi = "10.1109/ICHI.2019.8904506",

language = "英语",

series = "2019 IEEE International Conference on Healthcare Informatics, ICHI 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 IEEE International Conference on Healthcare Informatics, ICHI 2019",

address = "美国",

}

Wang, X, Chen, S, Li, T, Li, W, Zhou, Y, Zheng, J, Zhang, Y & Tang, B 2019, Assessing depression risk in Chinese microblogs: A corpus and machine learning methods. in 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019., 8904506, 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019, Institute of Electrical and Electronics Engineers Inc., 7th IEEE International Conference on Healthcare Informatics, ICHI 2019, Xi'an, China, 10/06/19. https://doi.org/10.1109/ICHI.2019.8904506

Assessing depression risk in Chinese microblogs: A corpus and machine learning methods. / Wang, Xiaofeng; Chen, Shuai; Li, Tao et al.
2019 IEEE International Conference on Healthcare Informatics, ICHI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8904506 (2019 IEEE International Conference on Healthcare Informatics, ICHI 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Assessing depression risk in Chinese microblogs

T2 - 7th IEEE International Conference on Healthcare Informatics, ICHI 2019

AU - Wang, Xiaofeng

AU - Chen, Shuai

AU - Li, Tao

AU - Li, Wanting

AU - Zhou, Yejie

AU - Zheng, Jie

AU - Zhang, Yaoyun

AU - Tang, Buzhou

PY - 2019/6

Y1 - 2019/6

N2 - Depression is a serious challenge for personal and public health. Tens of millions of people suffer from depression every year, but only a small part of them can receive professional treatment. Because people usually avoid disclosing or discussing mental health conditions, which are considered as stigma or taboo. Social network, such as the most popular Chinese microblogging website Sina Weibo, provides a channel for people, including depressed patients, to share their thoughts. We may find depressed emotions from microblogs. In this paper, we randomly collected a set of microblogs and manually annotated them with depression risk at level 0-3 (from mild to severe). On this corpus, we compared different machine learning methods for depression risk prediction, and provided benchmark results. The machine learning methods are support vector machine (SVM), convolutional neural network (CNN), long short-term memory network (LSTM) and bidirectional encoder representations from transformers (BERT).

AB - Depression is a serious challenge for personal and public health. Tens of millions of people suffer from depression every year, but only a small part of them can receive professional treatment. Because people usually avoid disclosing or discussing mental health conditions, which are considered as stigma or taboo. Social network, such as the most popular Chinese microblogging website Sina Weibo, provides a channel for people, including depressed patients, to share their thoughts. We may find depressed emotions from microblogs. In this paper, we randomly collected a set of microblogs and manually annotated them with depression risk at level 0-3 (from mild to severe). On this corpus, we compared different machine learning methods for depression risk prediction, and provided benchmark results. The machine learning methods are support vector machine (SVM), convolutional neural network (CNN), long short-term memory network (LSTM) and bidirectional encoder representations from transformers (BERT).

KW - Assessing depression risk

KW - Chinese microblogs

KW - Machine learning

UR - https://www.scopus.com/pages/publications/85075947554

U2 - 10.1109/ICHI.2019.8904506

DO - 10.1109/ICHI.2019.8904506

M3 - 会议稿件

AN - SCOPUS:85075947554

T3 - 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019

BT - 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 10 June 2019 through 13 June 2019

ER -

Wang X, Chen S, Li T, Li W, Zhou Y, Zheng J et al. Assessing depression risk in Chinese microblogs: A corpus and machine learning methods. In 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8904506. (2019 IEEE International Conference on Healthcare Informatics, ICHI 2019). doi: 10.1109/ICHI.2019.8904506

Assessing depression risk in Chinese microblogs: A corpus and machine learning methods

Abstract

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UN SDGs

Keywords

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