基于社交媒体数据的心理指标识别建模: 机器学习的方法

doi:10.3724/SP.J.1042.2021.00571

摘要/Abstract

摘要：

心理指标识别建模是基于海量数据结合计算机机器学习算法识别心理特征的一种新兴方式。由于传统纸笔测量方式所存在的诸多限制, 本文对基于社交媒体数据的心理建模方法及应用于心理测量的可行性进行综述, 介绍了特征及提取方法、常用机器学习算法以及应用场景, 并对心理指标识别建模的优势和不足进行了总结与展望。该测量方法基于社交媒体数据, 相比自我报告法具有时效性高、可回溯测量、生态效度好等独特优势。然而, 基于社交媒体的心理指标识别建模方法也在学习成本、硬件成本等方面存在局限性。未来研究人员需要进一步探索社会媒体信息与用户心理变量间的关联机制, 并将心理指标识别模型同传统心理学研究方法结合进行更多的探索和应用。心理指标识别建模结合心理测量基本原理和计算机领域机器学习的技术, 将为心理学研究打开一扇新的大门。

关键词: 心理建模, 心理测量, 社交媒体, 机器学习, 心理预测模型

Abstract:

Modeling psychological indexes (i.e., psych-modeling) is an emerging method that uses machine learning algorithms to identify psychological indexes based on big data. This paper reviews the feasibility of psych-modeling methods based on social media data in the field of psychometrics. Frequently used data types and machine learning algorithms are introduced. Then, we summarize psych-modeling's application to various scenarios together with its strengths and weaknesses. Compared with traditional self-reporting methods, psych-modeling has some advantages, including better performance in retrospective studies, greater ecological validity, and greater time-efficiency. However, psych-modeling has several limitations. For example, researchers need to spend extra time and effort to learn this new method and bear the inevitable cost of hardware. In future studies, researchers could investigate further how user's behavior on social media relates to psychological indexes. We also expect psych-modeling will be used in future psychological studies. By combining psychometrics and machine learning, we believe psych-modeling could make great contributions to psychology research and practice in the future.

Key words: psych-modeling, psychological measurement, social media, machine learning, psychological prediction model

中图分类号:

B841

苏悦, 刘明明, 赵楠, 刘晓倩, 朱廷劭. (2021). 基于社交媒体数据的心理指标识别建模: 机器学习的方法. 心理科学进展 , 29(4), 571-585.

SU Yue, LIU Mingming, ZHAO Nan, LIU Xiaoqian, ZHU Tingshao. (2021). Identifying psychological indexes based on social media data: A machine learning method. Advances in Psychological Science, 29(4), 571-585.

图/表 3

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数据类型	应用场景
数据类型	个人信息	人格	心理健康	其他
个人账户信息	分类: SVM、GP、LR	回归: M5、GPR、RR、线性回归、PACE 分类: SVM、NB、DT	回归: LASSO、SVR、stepwise
文本信息	回归: RR 分类: SVM、GP、LR、NB	回归: GPR、线性回归、RR、M5、RFR 分类: NB、SVM、ZeroR、RF、DT、ZeroR、J48、KNN、LR、NN	回归: 线性回归、LASSO、 SVR、stepwise、PACE 分类: SVM、LR、NN、RF	回归: RR、GPR [用户影响力] 分类: SVM [情感类别] LR [道德判断、自我监控行为]
社交网络信息	回归: 线性回归、RR 分类: LR、SVM、GP	回归: LASSO、GPR、线性回归、RFR、M5、PACE、RR 分类: SVM、NB、ZeroR、J48、RF、KNN、LR、NB、DT	回归: 线性回归、LASSO、SVR、stepwise、PACE 分类: SVM、NN	回归: RR、GPR [用户影响力] 分类: LR [政治倾向]
社交媒体使用信息		回归: 线性回归、PACE、GPR 分类: SVM、NB、DT、J48、RF、ZeroR	回归: 线性回归、PACE、LASSO、SVR、stepwise 分类: SVM、NN	回归: RR、GPR [用户影响力]
图片信息	分类: LR、NN	回归: 线性回归、RFR
其他信息	回归: PR、线性回归分类: SVM、LR、GP、NB、NN	回归: GPR、线性回归、RFR、LASSO 分类: NB、SVM、KNN、DT、ZeroR	回归: 线性回归	分类: RF [人类价值]

数据类型	应用场景
数据类型	个人信息	人格	心理健康	其他
个人账户信息	分类: SVM、GP、LR	回归: M5、GPR、RR、线性回归、PACE 分类: SVM、NB、DT	回归: LASSO、SVR、stepwise
文本信息	回归: RR 分类: SVM、GP、LR、NB	回归: GPR、线性回归、RR、M5、RFR 分类: NB、SVM、ZeroR、RF、DT、ZeroR、J48、KNN、LR、NN	回归: 线性回归、LASSO、 SVR、stepwise、PACE 分类: SVM、LR、NN、RF	回归: RR、GPR [用户影响力] 分类: SVM [情感类别] LR [道德判断、自我监控行为]
社交网络信息	回归: 线性回归、RR 分类: LR、SVM、GP	回归: LASSO、GPR、线性回归、RFR、M5、PACE、RR 分类: SVM、NB、ZeroR、J48、RF、KNN、LR、NB、DT	回归: 线性回归、LASSO、SVR、stepwise、PACE 分类: SVM、NN	回归: RR、GPR [用户影响力] 分类: LR [政治倾向]
社交媒体使用信息		回归: 线性回归、PACE、GPR 分类: SVM、NB、DT、J48、RF、ZeroR	回归: 线性回归、PACE、LASSO、SVR、stepwise 分类: SVM、NN	回归: RR、GPR [用户影响力]
图片信息	分类: LR、NN	回归: 线性回归、RFR
其他信息	回归: PR、线性回归分类: SVM、LR、GP、NB、NN	回归: GPR、线性回归、RFR、LASSO 分类: NB、SVM、KNN、DT、ZeroR	回归: 线性回归	分类: RF [人类价值]

计算模型	重测信度
大五人格	0.77~0.79
抑郁	0.83
自杀可能性	0.80~0.91
生活满意度	0.84

计算模型	重测信度
大五人格	0.77~0.79
抑郁	0.83
自杀可能性	0.80~0.91
生活满意度	0.84