Predicting Human Trustfulness from Facebook Language

TL;DR

This paper develops a language-based method to assess trustfulness from Facebook posts, linking linguistic features to questionnaire scores and exploring factors like questionnaire size and word count to improve prediction accuracy.

Contribution

It introduces the first language-based assessment of trustfulness and examines how questionnaire size and word count influence model performance.

Findings

Longer questionnaires improve test accuracy.

Including users with smaller questionnaires benefits training.

Word count positively impacts individual prediction accuracy.

Abstract

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.