Abstract
(b) Questionnaire based outcomes We show mean Pearson R over 10 random train-test splits. Language based factors (FA) do not outperform questionnaire based factors.
['[BOLD] Method', 'Big5Questions', 'Sat. W/ Life', 'Depression']
For questionnaire-based outcomes, the Big5 factors better predict SWL and depression than our factors. This is driven by strong correlations of SWL and depression with extraversion and neuroticism. The Big5 also outpredicts our factors for the size of the friend network, which again is driven by strong correlations with extraversion. Notably, correlations are equivalent for the Big5 and FA5 in predicting the additional personality questions (Big5Questions). In contrast, as highlighted in the table, our factors better predict income, IQ, and the user likes. For example, on the task of predicting Likes, FA with 5 factors outperforms the baseline by 7 percentage points (60.11 - 52.6). It is worth emphasizing here that the task of predicting Likes is a challenging task - it essentially involves 20 different classification tasks. Consequently, an improvement of 7 percentage points on this task is promising. Also note that adding age and gender as covariates improves predictive performance (compare FA5+Demog with FA5).