Predicting active users' personality based on micro-blogging behaviors.
Because 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 839 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 [corrected]. 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.
Institute of Psychology, Chinese Academy of Sciences, Beijing, China ; School of Computer and Control, University of Chinese Academy of Sciences, Beijing, China.
Lin
L
Li
Institute of Psychology, Chinese Academy of Sciences, Beijing, China ; School of Computer and Control, University of Chinese Academy of Sciences, Beijing, China.
Ang
A
Li
Institute of Psychology, Chinese Academy of Sciences, Beijing, China ; School of Computer and Control, University of Chinese Academy of Sciences, Beijing, China.
Bibo
B
Hao
Institute of Psychology, Chinese Academy of Sciences, Beijing, China ; School of Computer and Control, University of Chinese Academy of Sciences, Beijing, China.
Zengda
Z
Guan
Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Tingshao
T
Zhu
D000328:Adult; D057218:Blogging / Q000145:classification / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008960:Models, Psychological; D010552:Personality Assessment / Q000592:standards / Q000706:statistics & numerical data*; D010555:Personality Inventory / Q000592:standards / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D060388:Support Vector Machine; D011795:Surveys and Questionnaires; D055815:Young Adult