Li Lin, Li Ang, Hao Bibo, Guan Zengda, Zhu Tingshao
Institute of Psychology, Chinese Academy of Sciences, Beijing, China ; School of Computer and Control, University of Chinese Academy of Sciences, Beijing, China.
Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
PLoS One. 2014 Jan 22;9(1):e84997. doi: 10.1371/journal.pone.0084997. eCollection 2014.
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.
由于其丰富性和可得性，微博已成为进行心理学研究的理想平台。在本文中，我们提议通过微博行为来预测活跃用户的人格特质。547名中国微博活跃用户参与了本研究。他们的人格特质通过大五人格量表进行测量，微博行为的数字记录通过网络爬虫收集。在提取了839个微博行为特征后，我们首先使用支持向量机（SVM）训练分类模型，区分大五人格量表各维度上得分高和得分低的参与者[校正后]。分类准确率在84%至92%之间。我们还使用PaceRegression方法建立了回归模型，预测参与者在大五人格量表各维度上的得分。预测得分与实际得分之间的皮尔逊相关系数在0.48至0.54之间。结果表明，活跃用户的人格特质可以通过微博行为来预测。
