dataset of Facebook profiles and traits almost identical to those in the myPersonality dataset [@nyt]. The week after CA’s project became public, Facebook’s stock plummeted \$75 billion [@marketwatch]. One factor in that drop was the belief that Facebook had allowed a third party to create a powerful marketing tool that could manipulate elections [@guardianBannon; @nyt]. There are dozens of publications on the myPersonality dataset. However, this is the first to predict SIQ, fair-mindedness, and self-disclosure, which CA discussed in relation to building user models [@nyt]. Besides performance benchmarks, the other major contribution of this paper are the most highly weighted words to predict each trait. The weights also say something about human behavior. The interpretation here is more complex: regression on tens of thousands of features is fraught with over-fitting and colinearity. Despite those problems, in Section \[interpret\] we argue that the weights can still be treated as a data exploration tool similar to clustering. We provide examples of previously studied relationships that are borne out in the word lists, and believe the lists are a useful tool to develop yet unstudied hypotheses.

Highly weighted features are also an important way to analyze models. We argue in section \[CA\] that 