can be extracted from Facebook statuses. Our reported performance is useful to learn how traits are related to online behavior. For example, sensational interests as measured by the Sensational Interest Questionnaire (SIQ) have been studied for internal reliability [@egan1999sensational], relationship to physical aggression [@egan2009sensational], and role in intrasexual competition [@weiss2004sensational]. Yet work connecting SIQ with social media use relies on individually labeling sensational interests in statuses and is only predictive among males [@hagger2011social]. Our model performs well for both males and females without hand-labeling statuses. Similarly, other research found no relationship between satisfaction with life (SWL) and status updates [@wang2014can]; we show modest test set performance. Finally, although Facebook Likes have been shown to be highly predictive of many personal traits [@kosinski2013private], language models with good performance on this dataset have been limited to predicting personality, age, and gender [@schwartz2013personality; @farnadi2016computational; @sap2014developing].

The benchmark also helps assess the efficacy of services that explicitly or implicitly rely on inferring these traits. This is valuable to those developing new services as well as to users concerned about privacy. Of particular interest is the role of psychographic models in Cambridge Analytica’s (CA) marketing strategy. From leaked internal communications, in 2014 CA amassed a 