  Trustfulness -- one's general tendency to have confidence in unknown people
or situations -- predicts many important real-world outcomes such as mental
health and likelihood to cooperate with others such as clinicians. While
data-driven measures of interpersonal trust have previously been introduced,
here, we develop the first language-based assessment of the personality trait
of trustfulness by fitting one's language to an accepted questionnaire-based
trust score. Further, using trustfulness as a type of case study, we explore
the role of questionnaire size as well as word count in developing
language-based predictive models of users' psychological traits. We find that
leveraging a longer questionnaire can yield greater test set accuracy, while,
for training, we find it beneficial to include users who took smaller
questionnaires which offers more observations for training. Similarly, after
noting a decrease in individual prediction error as word count increased, we
found a word count-weighted training scheme was helpful when there were very
few users in the first place.
