Recreating the Relationship between Subjective Wellbeing and Personality Using Machine Learning: An Investigation into Facebook Online Behaviours

Abstract

Abstract:The twenty-first century has delivered technological advances that allow researchers to utilise social media to predict personal traits and psychological constructs. This article aims to further our understanding of the relationship between subjective wellbeing (SWB) and the Five Factor Model (FFM) of personality by attempting to replicate the relationship using machine learning prediction models. Data from the myPersonality Project was used; with observed SWB scores derived from the Satisfaction With Life Scale (SWLS) and Five Factor Model (FFM) personality profiles generated using responses on the 100-item IPIP proxy of the NEO-PI-R. After data cleaning, FFM personality traits and SWB scores were predicted by reducing Facebook Likes into 50 dimensions using SVD and then running the data through six multiple regressions (fitting the model via least squares and splitting the data via k-folds validation) with the Likes dimensions as predictors and each of the FFM traits and the SWB score as response variables. Standard multiple regression analyses were conducted for the observed and machine learning predicted variables to compare the relationships in the context of previous literature. The results revealed that in the observed model, high SWB was predicted by high extraversion, conscientiousness, and agreeableness, and low openness to experience and neuroticism as per previous research. For the machine learning model, high SWB was predicted by high extraversion, openness to experience, conscientiousness, and agreeableness, and low neuroticism. The relationships between SWB and extraversion, neuroticism, and conscientiousness were successfully replicated in the machine learning model. Openness to experience changed direction in its relationship with SWB from the observed to machine learning-derived variables due to failure to accurately recreate the variable, and agreeableness was multicollinear with SWB in the machine learning model due to the unknowing use of identical digital behaviours to replicate each construct. Implications of the results and directions for future research are discussed.

How Well Can an AI Chatbot Infer Personality? Examining Psychometric Properties of Machine-inferred Personality Scores

“…Although machine-inferred personality manifest domain scores also showed good convergent validity, discriminant validity was less impressive, particularly in the test sample. Nevertheless, overall, our results show improvements in differentiating among different traits in the machineinferred personalities as compared to existing machine learning applications (e.g., Hickman et al, 2022;Marinucci et al, 2018;Park et al, 2015). Table 7 also shows that C1, D1, and D2 (D2a) dropped around .10 from the training to testing sample at both the latent and manifest variable levels.…”

“…We identified four empirical studies that examined the discriminant validity of machineinferred personality scores (Harrison et al, 2019;Hickman et al, 2022;Marinucci et al, 2018;Park et al, 2015), with findings suggesting relatively poor discriminant validity. For instance, Park et al (2015) showed that the average correlations among Big Five domain scores were significantly higher when measured by the ML method than by self-report questionnaires (𝑟𝑟̅ = .29 vs. 𝑟𝑟̅ = .19).…”

How well can an AI chatbot infer personality? Examining psychometric properties of machine-inferred personality scores.

“…Although machine-inferred personality manifest domain scores also showed good convergent validity, discriminant validity was less impressive, particularly in the test sample. Nevertheless, overall, our results show improvements in differentiating among different traits in the machine-inferred personalities as compared to existing machine learning applications (e.g., Hickman et al, 2022; Marinucci et al, 2018; Park et al, 2015). Table 7 also shows that C1, D1, and D2 (D2a) dropped around .10 from the training to testing sample at both the latent and manifest variable levels.…”

“…Another important finding is that the current study, which was based on self-report models, yielded better psychometric properties (e.g., substantially higher convergent validity and cross-sample generalizability) of machine-inferred personality scores than many similar ML studies that were also based on self-report models (e.g., Hickman et al, 2022;Marinucci et al, 2018). We offer several tentative explanations that might help reconcile this inconsistency.…”

“…We identified four empirical studies that examined the discriminant validity of machine-inferred personality scores (Harrison et al, 2019; Hickman et al, 2022; Marinucci et al, 2018; Park et al, 2015), with findings suggesting relatively poor discriminant validity. For instance, Park et al (2015) showed that the average correlations among Big Five domain scores were significantly higher when measured by the ML method than by self-report questionnaires (rfalse¯ = .29 vs. rfalse¯ = .19).…”