into chunks of no more than 4000 characters, and no less than 1000, as this is enough text for humans to perform gender classification [@nguyen2014gender]. Each chunk contains roughly 800 words. Chunks from the same user are assigned entirely to either the training or test set. Unfortunately, preprocessing differences do not allow for a direct comparison between methods. However, enforcing the same preprocessing for both models would necessarily limit one.

Labels
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Tables \[acc\_cont\] and \[acc\_cat\] provide statistics of the continuous and categorical data respectively. What follows is a brief description of each label and how it was collected.

### Gender

is the binary label users supplied when setting up their Facebook account. Offering this information was common before 2008, and mandatory from 2008-2014. In 2014, (after the collection of this dataset) Facebook added 56 more gender options but still uses a binary representation to monetize users [@bivens2017gender].

### Race

labels provided in the dataset are inferred from profile pictures using the Faceplusplus.com algorithm which can identify races termed White, Black, and Asian. A noisy measure of visual phenotype is not the gold standard for the study of race, however, our results indicate it is related to social media use.

