How social media expression can reveal personality

Frontiers in Psychiatry

Psychiatry

Xiaoqian Liu¹  Feng Huang¹  Yue Su²  Tingshao Zhu²  Nuo Han³  Sijia Li⁴  Linyan Li⁵  Yeye Wen⁶ 

[1] Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China;Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China;Department of Psychology, University of Chinese Academy of Sciences, Beijing, China;Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China;Department of Psychology, University of Chinese Academy of Sciences, Beijing, China;School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China;Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong, Hong Kong SAR, China;School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China;Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, Hong Kong SAR, China;School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China;

关键词: personality;    social media;    machine learning;    domain knowledge;    psychological lexicons;    mental health;    Big Five;   

DOI  :  10.3389/fpsyt.2023.1052844

 received in 2022-09-24, accepted in 2023-02-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundPersonality psychology studies personality and its variation among individuals and is an essential branch of psychology. In recent years, machine learning research related to personality assessment has started to focus on the online environment and showed outstanding performance in personality assessment. However, the aspects of the personality of these prediction models measure remain unclear because few studies focus on the interpretability of personality prediction models. The objective of this study is to develop and validate a machine learning model with domain knowledge introduced to enhance accuracy and improve interpretability.MethodsStudy participants were recruited via an online experiment platform. After excluding unqualified participants and downloading the Weibo posts of eligible participants, we used six psycholinguistic and mental health-related lexicons to extract textual features. Then the predictive personality model was developed using the multi-objective extra trees method based on 3,411 pairs of social media expression and personality trait scores. Subsequently, the prediction model’s validity and reliability were evaluated, and each lexicon’s feature importance was calculated. Finally, the interpretability of the machine learning model was discussed.ResultsThe features from Culture Value Dictionary were found to be the most important predictors. The fivefold cross-validation results regarding the prediction model for personality traits ranged between 0.44 and 0.48 (p < 0.001). The correlation coefficients of five personality traits between the two “split-half” datasets data ranged from 0.84 to 0.88 (p < 0.001). Moreover, the model performed well in terms of contractual validity.ConclusionBy introducing domain knowledge to the development of a machine learning model, this study not only ensures the reliability and validity of the prediction model but also improves the interpretability of the machine learning method. The study helps explain aspects of personality measured by such prediction models and finds a link between personality and mental health. Our research also has positive implications regarding the combination of machine learning approaches and domain knowledge in the field of psychiatry and its applications to mental health.

【 授权许可】

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Copyright © 2023 Han, Li, Huang, Wen, Su, Li, Liu and Zhu.