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The language of character strengths: Predicting morally valued traits on social media


Pang, Dandan; Eichstaedt, Johannes C; Buffone, Anneke; Slaff, Barry; Ruch, Willibald; Ungar, Lyle H (2020). The language of character strengths: Predicting morally valued traits on social media. Journal of Personality, 88(2):287-306.

Abstract

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 (r = 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.

Abstract

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 (r = 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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
Dewey Decimal Classification:150 Psychology
Language:English
Date:1 April 2020
Deposited On:26 Jun 2019 12:36
Last Modified:03 Mar 2020 02:01
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0022-3506
Additional Information:This is the peer reviewed version of the following article: Pang D, Eichstaedt JC, Buffone A, Slaff B, Ruch W, Ungar LH. The language of character strengths: Predicting morally valued traits on social media. Journal of Personality. 2019;00:1– 20, which has been published in final form at https://doi.org/10.1111/jopy.12491. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. (http://www.wileyauthors.com/self-archiving)
OA Status:Closed
Publisher DOI:https://doi.org/10.1111/jopy.12491
PubMed ID:31107975
Project Information:
  • : FunderSNSF
  • : Grant IDP0ZHP1_165465
  • : Project TitleThe mutual support model of mindfulness and character strengths and a new perspective on emotion regulation of mindfulness
  • : FunderTempleton Religion Trust
  • : Grant IDTRT0084
  • : Project Title

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