a militarism predictor CA may have built is accurate, but extracts obvious features. Additionally, by inspecting the features in an Atheist vs. Agnostic classifier we find many gendered words. We demonstrate the bias empirically, then fix the classifier to be more fair. This approach is instructive for interrogating more critical models built on social media data.

This paper includes many contributions that could stand alone. We show that the text of Facebook statuses can predict user SWL and SIQ. We expand the prediction of political identity from a single spectrum (liberal/conservative) to twelve distinct ideologies with varying levels of overlap and popularity. On that task, we establish state of the art performance with a model that also provides informative features for every pairwise political comparison. We recreate models CA may have built, and report their performance and the type of information they extracted. We bring character level deep learning to gender prediction. To our knowledge, we also set the standard for predicting IQ, fair-mindedness, self-disclosure, race, and religion from Facebook statuses. Finally, we propose a novel method to make classification less biased.

Given the broad scope of this paper, some contributions are given less space than they would typically merit. 