Abstract
(a) Behavioral/Economic outcomes: We show mean Pearson’s R over 10 random train-test splits for FriendSize, Income and IQ while for Likes we show the mean area under the curve (AUC) over all 20 categories. Language based factors (FA) perform competitively and even outperform questionnaire based factors as highlighted in color.
['[BOLD] Method', 'FriendSize', 'Income', 'IQ', 'Likes']
In this section, we provide supporting results based on our analyses of BLTs. For comparison, with the questionnaire items, we calculate the 10 aspect scores and 30 facet based scores, using the relevant IPIP items. BLTs consistently outperform questionnaire based models on behavioral/demographic outcomes, and underperform on questionnaire based outcomes. Similar results are also obtained with demographics residualized (see