  This paper explores the use of language models to predict 20 human traits
from users' Facebook status updates. The data was collected by the
myPersonality project, and includes user statuses along with their personality,
gender, political identification, religion, race, satisfaction with life, IQ,
self-disclosure, fair-mindedness, and belief in astrology. A single
interpretable model meets state of the art results for well-studied tasks such
as predicting gender and personality; and sets the standard on other traits
such as IQ, sensational interests, political identity, and satisfaction with
life. Additionally, highly weighted words are published for each trait. These
lists are valuable for creating hypotheses about human behavior, as well as for
understanding what information a model is extracting. Using performance and
extracted features we analyze models built on social media. The real world
problems we explore include gendered classification bias and Cambridge
Analytica's use of psychographic models.
