How social media expression can reveal personality - Han, Nuo; Li, Sijia; Huang, Feng; Wen, Yeye; Su, Yue; Li, Linyan; Liu, Xiaoqian; Zhu, Tingshao (Frontiers in Psychiatry, 2023)
Background: Personality 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. Methods: Study participants were recruited via an online experiment platform. After excluding unqualified participants and conducting data processing, 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. Results: The 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. Conclusion: By 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. Our research also has positive implications regarding the introduction of machine learning approaches into the field of psychology and also regarding the applications on mental health. It helps explain the aspects of personality that such prediction models measure.