Identifying psychological indexes based on social media data: A machine learning method

doi:10.3724/SP.J.1042.2021.00571

Abstract

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

CLC Number:

B841

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

Figures/Tables 3

References 129

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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