classifier in Section \[gender\_bias\]. Binary labels were constructed by pooling {‘catholic’, ‘christian-catholic’, ‘romancatholic’, ‘christian’, ‘christian-baptist’} and {‘atheist’, ‘agnostic’,‘none’ }. We achieve 78.0% accuracy, 5.2% above baseline. Those words are on table \[words\_rel\_pol\]. To our knowledge, there is no other multi class religion predictor to which our results can be compared.

### IQ {#IQ}

In a genome wide association meta study of 78,308 individuals, 336 single nucleotide polymorphisms were found to explain 2.1-4.8% of the IQ variance among the test population [@sniekers2017genome]. We achieve 12.8% EV with a model trained on less than 2000 users and their statuses. Using $\ell_1$ regularization to limit the vocabulary to the ten most informative words—final, physics; ayaw, family, friend, heart, lmao, nite, strong, ur—still yields 5.6% percent EV. The relative accuracy of such a trivial model that leverages intuitive features is a helpful comparison for any project predicting this important trait. To our knowledge, this is the only work to date that infers IQ from social media.

The selected features are also informative. Words suggesting intelligence—‘final’ and ‘physics’—are parsimonious and singularly academic. Whereas the university experience is sufficient to find users with high IQ, features inversely related to IQ are more focused on disposition. From 