PSYCH OpenIR  > 社会与工程心理学研究室
Predicting Depression of Social Media User on Different Observation Windows
Hu, Q (Hu, Quan)1; Li, A (Li, Ang)2,3; Heng, F (Heng, Fei)4; Li, JP (Li, Jianpeng)4; Zhu, TS (Zhu, Tingshao)5
First AuthorHu, Q (Hu, Quan)
2015
Conference NameIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)
Source PublicationIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)
Volume1
Issue不详
Pages361-364
Conference DateDEC 06-09, 2015
Conference PlaceSingapore, SINGAPORE
Abstract

Depression has become a public health concern around the world. Traditional methods for detecting depression rely on self-report techniques, which suffer from inefficient data collection and processing. This paper built both classification and regression models based on linguistic and behavioral features acquired from 10,102 social media users, and compared classification and prediction accuracy respectively among models built on different observation windows. Results showed that users' depression can be predicted via social media. The best result appears when we make prediction in advance for half a month with a 2-month length of observation time.

KeywordMachine Learning Depression Microblogging Behavior Classification Prediction
DOI10.1109/WI-IAT.2015.166
Language英语
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.psych.ac.cn/handle/311026/26555
Collection社会与工程心理学研究室
Affiliation1.Henan Univ, Inst Psychol, Chinese Acad Sci, Sch Comp & Informat Engn, Kaifeng, Peoples R China
2.Beijing Forestry Univ, Dept Psychol, Beijing, Peoples R China
3.Univ New South Wales, Black Dog Inst, Sydney, NSW 2052, Australia
4.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Inst Psychol, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Hu, Q ,Li, A ,Heng, F ,et al. Predicting Depression of Social Media User on Different Observation Windows[C],2015:361-364.
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