TY - GEN
T1 - Predicting human trustfulness from Facebook language
AU - Zamani, Mohammadzaman
AU - Buffone, Anneke
AU - Schwartz, H. Andrew
N1 - Publisher Copyright:
© 2018 Association for Computational Linguistics
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85119960903
M3 - Conference contribution
AN - SCOPUS:85119960903
T3 - Proceedings of the 5th Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, CLPsych 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018
SP - 174
EP - 181
BT - Proceedings of the 5th Workshop on Computational Linguistics and Clinical Psychology
A2 - Loveys, Kate
A2 - Niederhoffer, Kate
A2 - Prud�hommeaux, Emily
A2 - Resnik, Rebecca
A2 - Resnik, Philip
PB - Association for Computational Linguistics (ACL)
T2 - 5th Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, CLPsych 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018
Y2 - 5 June 2018
ER -