) 1中国科学院心理研究所, 北京 100101
2中国科学院大学心理学系, 北京 100049
3联想研究院, 北京 100094
收稿日期:2020-01-14出版日期:2021-04-15发布日期:2021-02-22基金资助:国家社科基金重点项目(17AZD041);国家自然科学基金项目(31700984);中国科学院青年创新促进会资助Identifying psychological indexes based on social media data: A machine learning method
SU Yue1,2, LIU Mingming1,3, ZHAO Nan1, LIU Xiaoqian1, ZHU Tingshao1,2() 1Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
2Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
3Lenovo Research, Beijing 100094, China
Received:2020-01-14Online:2021-04-15Published:2021-02-22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。
图/表 3
图1心理建模的一般过程示意图
图1心理建模的一般过程示意图表1心理建模常用特征-场景-算法组合汇总
| 数据 类型 | 应用场景 | |||
|---|---|---|---|---|
| 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] |
表1心理建模常用特征-场景-算法组合汇总
| 数据 类型 | 应用场景 | |||
|---|---|---|---|---|
| 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] |
表2心理模型识别信度检验(刘明明, 2019)
| 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 |
表2心理模型识别信度检验(刘明明, 2019)
| 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 |
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