The language of character strengths: Predicting morally valued traits on social media.
- Pang D Department of Work and Organizational Psychology, University of Bern, Bern, Switzerland.
- Eichstaedt JC Positive Psychology Center, University of Pennsylvania, Philadelphia, Pennsylvania.
- Buffone A Positive Psychology Center, University of Pennsylvania, Philadelphia, Pennsylvania.
- Slaff B Positive Psychology Center, University of Pennsylvania, Philadelphia, Pennsylvania.
- Ruch W Personality and Assessment, Department of Psychology, University of Zurich, Zurich, Switzerland.
- Ungar LH Positive Psychology Center, University of Pennsylvania, Philadelphia, Pennsylvania.
- 2019-05-21
Published in:
- Journal of personality. - 2020
English
OBJECTIVE
Social media is increasingly being used to study psychological constructs. This study is the first to use Twitter language to investigate the 24 Values in Action Inventory of Character Strengths, which have been shown to predict important life domains such as well-being.
METHOD
We use both a top-down closed-vocabulary (Linguistic Inquiry and Word Count) and a data-driven open-vocabulary (Differential Language Analysis) approach to analyze 3,937,768 tweets from 4,423 participants (64.3% female), who answered a 240-item survey on character strengths.
RESULTS
We present the language profiles of (a) a global positivity factor accounting for 36% of the variances in the strengths, and (b) each of the 24 individual strengths, for which we find largely face-valid language associations. Machine learning models trained on language data to predict character strengths reach out-of-sample prediction accuracies comparable to previous work on personality (rmedian = 0.28, ranging from 0.13 to 0.51).
CONCLUSIONS
The findings suggest that Twitter can be used to characterize and predict character strengths. This technique could be used to measure the character strengths of large populations unobtrusively and cost-effectively.
Social media is increasingly being used to study psychological constructs. This study is the first to use Twitter language to investigate the 24 Values in Action Inventory of Character Strengths, which have been shown to predict important life domains such as well-being.
METHOD
We use both a top-down closed-vocabulary (Linguistic Inquiry and Word Count) and a data-driven open-vocabulary (Differential Language Analysis) approach to analyze 3,937,768 tweets from 4,423 participants (64.3% female), who answered a 240-item survey on character strengths.
RESULTS
We present the language profiles of (a) a global positivity factor accounting for 36% of the variances in the strengths, and (b) each of the 24 individual strengths, for which we find largely face-valid language associations. Machine learning models trained on language data to predict character strengths reach out-of-sample prediction accuracies comparable to previous work on personality (rmedian = 0.28, ranging from 0.13 to 0.51).
CONCLUSIONS
The findings suggest that Twitter can be used to characterize and predict character strengths. This technique could be used to measure the character strengths of large populations unobtrusively and cost-effectively.
- Language
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- English
- Open access status
- hybrid
- Identifiers
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- DOI 10.1111/jopy.12491
- PMID 31107975
- Persistent URL
- https://sonar.rero.ch/global/documents/14595
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