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Predicting Active Users' Personality Based on Micro-Blogging Behaviors
Li, Lin1,2; Li, Ang1,2; Hao, Bibo1,2; Guan, Zengda1,2; Zhu, Tingshao1
2014-01-22
Source PublicationPLOS ONE
ISSN1932-6203
SubtypeArticle
Volume9Issue:1Pages:1-11
AbstractBecause of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 845 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors.
Indexed BySCI
Language英语
WOS IDWOS:000330283100019
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/13443
Collection社会与工程心理学研究室
Affiliation1.Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Li, Lin,Li, Ang,Hao, Bibo,et al. Predicting Active Users' Personality Based on Micro-Blogging Behaviors[J]. PLOS ONE,2014,9(1):1-11.
APA Li, Lin,Li, Ang,Hao, Bibo,Guan, Zengda,&Zhu, Tingshao.(2014).Predicting Active Users' Personality Based on Micro-Blogging Behaviors.PLOS ONE,9(1),1-11.
MLA Li, Lin,et al."Predicting Active Users' Personality Based on Micro-Blogging Behaviors".PLOS ONE 9.1(2014):1-11.
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