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Tracing Personality Structure in Narratives: A Computational Bottom-Up Approach to Unpack Writers, Characters, and Personality in Historical Context

Fischer, Ronald orcid logoORCID: 0000-0002-3055-3955, Karl, Johannes Alfons orcid logoORCID: 0000-0001-5166-0728, Luczak-Roesch, Markus orcid logoORCID: 0000-0003-4610-7244, Fetvadjiev, Velichko H. orcid logoORCID: 0000-0002-5475-5121 and Grener, Adam (2020) Tracing Personality Structure in Narratives: A Computational Bottom-Up Approach to Unpack Writers, Characters, and Personality in Historical Context. European Journal of Personality, 34 (5). pp. 917-943. ISSN 0890-2070

Abstract
We present a new method for personality assessment at a distance to uncover personality structure in historical texts. We focus on how two 19th century authors understood and described human personality; we apply a new bottom-up computational approach to extract personality dimensions used by Jane Austen and Charles Dickens to describe fictional characters in 21 novels. We matched personality descriptions using three person-description dictionaries marker scales as reference points for interpretation. Factor structures did not show strong convergence with the contemporary Big Five model. Jane Austen described characters in terms of social and emotional richness with greater nuances but using a less extensive vocabulary. Charles Dickens, in contrast, used a rich and diverse personality vocabulary, but those descriptions centred around more restricted dimensions of power and dominance. Although we could identify conceptually similar factors across the two authors, analyses of the overlapping vocabulary between the two authors suggested only moderate convergence. We discuss the utility and potential of automated text analysis and the lexical hypothesis to (i) provide insights into implicit personality models in historical texts and (ii) bridge the divide between idiographic and nomothetic perspectives.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:personality; five-factor model; idiographic analysis; automated text analysis; transcendental; information cascades
Subjects:Computer Science > Computational linguistics
Humanities > Literature
Medical Sciences > Psychology
Social Sciences > Social psychology
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Psychology
Publisher:SAGE Publications
Official URL:https://dx.doi.org/10.1002/per.2270
Copyright Information:© 2020 European Association of Personality Psychology
ID Code:27425
Deposited On:27 Jul 2022 13:38 by Johannes Karl . Last Modified 18 Jan 2023 13:33
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