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.
Highlights
Fast-paced technological trends demand research tools in psychology to evolve
For the original model with observed variable scores, high subjective wellbeing (SWB) was predicted by high extraversion, agreeableness, and conscientiousness, and low openness to experience and neuroticism
For the machine learning model, high SWB was predicted by high extraversion, openness to experience, conscientiousness, and agreeableness, and low neuroticism
Summary
There has been a historical focus on self-report methods and traditional behaviour analysis due to their ease of use and proliferation in psychological research Novel approaches such as machine learning and data mining have recently begun to gain traction in psychological research [1]. Facebook ‘Like’ data was first reduced via unsupervised feature extraction using singular value decomposition, employing the dimensions and pre-labelled personality (FFM) and SWB data, and linear regression and k-folds validation were used to predict participants’ personality (FFM) and SWB. These predicted values were used to recreate the relationship between the FFM and SWB, and assess the accuracy of the prediction model compared to observed scores
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