is more restrictive and only includes users with at least 1000 words. Notwithstanding these discrepancies, which may boost or dampen performance, the results are very similar. When the LIWC representation is added to the tri-grams, there is a slight improvement to 91.6% accuracy. Preprocessing is even less similar for the char-CNN described in the Section \[CNN\]. The human baseline of 84.0% consists of volunteer judgments based on 20-40 user tweets as reported by Nguyen et al [@nguyen2014gender]. This is less text than is available to the other models, and from a different social media platform. But, with 210 volunteer guesses per user, it provides a relevant human baseline.

### Personality

After gender, personality is the most studied trait in this paper. Likewise, Schwartz et al achieve the best results to date [@schwartz2013personality]. They report the square root of EV to two significant digits: 0.42, 0.35, 0.38, 0.31, 0.31. In that format, we are just 0.01 beneath the state of the art for openness and agreeableness, 0.01 better for neuroticism, and equivalent for the remaining traits. As with gender, we achieve this with a simpler model.

### Political Identity

Prediction accuracy of 33.7% is a gain of 11.7% over the baseline 