@inproceedings{zamani-etal-2018-predicting,
title = "Predicting Human Trustfulness from {F}acebook Language",
author = "Zamani, Mohammadzaman and
Buffone, Anneke and
Schwartz, H. Andrew",
editor = "Loveys, Kate and
Niederhoffer, Kate and
Prud{'}hommeaux, Emily and
Resnik, Rebecca and
Resnik, Philip",
booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
month = jun,
year = "2018",
address = "New Orleans, LA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0619",
doi = "10.18653/v1/W18-0619",
pages = "174--181",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Predicting Human Trustfulness from Facebook Language
%A Zamani, Mohammadzaman
%A Buffone, Anneke
%A Schwartz, H. Andrew
%Y Loveys, Kate
%Y Niederhoffer, Kate
%Y Prud’hommeaux, Emily
%Y Resnik, Rebecca
%Y Resnik, Philip
%S Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, LA
%F zamani-etal-2018-predicting
%X 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.
%R 10.18653/v1/W18-0619
%U https://aclanthology.org/W18-0619
%U https://doi.org/10.18653/v1/W18-0619
%P 174-181
Markdown (Informal)
[Predicting Human Trustfulness from Facebook Language](https://aclanthology.org/W18-0619) (Zamani et al., CLPsych 2018)
ACL
- Mohammadzaman Zamani, Anneke Buffone, and H. Andrew Schwartz. 2018. Predicting Human Trustfulness from Facebook Language. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pages 174–181, New Orleans, LA. Association for Computational Linguistics.