Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features-Reference-Cited by-同舟云学术

Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features

Published:2020-08-01 Issue:8 Volume:E103.D Page:1825-1832
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

ALSAGRI Hatoon S.¹,YKHLEF Mourad²

Affiliation:

1. Dept. of Information Systems, Al Imam Mohammad Ibn Saud Islamic University (IMSIU)

2. Information Systems Department, College of Computer and Information Sciences, King Saud University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E103.D/8/E103.D_2020EDP7023/_pdf

Reference35 articles.

1. [1] G. Shen, J. Jia, L. Nie, F. Feng, C. Zhang, T. Hu, T.-S. Chua, and W. Zhu, “Depression detection via harvesting social media: A multimodal dictionary learning solution,” IJCAI, pp.3838-3844, 2017. 10.24963/ijcai.2017/536

2. [2] D. Mowery, C. Bryan, and M. Conway, “Feature studies to inform the classification of depressive symptoms from twitter data for population health,” arXiv preprint arXiv:1701.08229, 2017.

3. [3] G. Coppersmith, M. Dredze, and C. Harman, “Quantifying mental health signals in twitter,” Proceedings of the workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality, pp.51-60, 2014. 10.3115/v1/w14-3207

4. [4] S.J. Stack, “Mental illness and suicide,” The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society, pp.1618-1623, 2014. 10.1002/9781118410868.wbehibs067

5. [5] M. De Choudhury, E. Kiciman, M. Dredze, G. Coppersmith, and M. Kumar, “Discovering shifts to suicidal ideation from mental health content in social media,” Proceedings of the 2016 CHI conference on human factors in computing systems, pp.2098-2110, ACM, 2016. 10.1145/2858036.2858207

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