Predicting life engagement and happiness from gaming motives and primary emotional traits before and during the COVID pandemic: a machine learning approach

Abstract

AbstractThe present study investigated whether life engagement and happiness can be predicted from gaming motives and primary emotional traits. Two machine learning algorithms (random forest model and one-dimensional convolutional neural network) were applied using a dataset from before the COVID-19 pandemic as the training dataset. The algorithms derived were then applied to test if they would be useful in predicting life engagement and happiness from gaming motives and primary emotional systems on a dataset collected during the pandemic. The best prediction values were observed for happiness with ρ = 0.758 with explained variance of R2 = 0.575 when applying the best performing algorithm derived from the pre-COVID dataset to the COVID dataset. Hence, this shows that the derived algorithm based on the pre-pandemic data set, successfully predicted happiness (and life engagement) from the same set of variables during the pandemic. Overall, this study shows the feasibility of applying machine learning algorithms to predict life engagement and happiness from gaming motives and primary emotional systems.