Special Issue Article
Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining
Louis Tay,
Sang Eun Woo,
Louis Hickman,
Rachel M. Saef,
Corresponding Author
Louis Tay
Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
Correspondence to: Louis Tay, Department of Psychological Sciences, Purdue University, 703 Third Street, West Lafayette, IN 47907, USA.
E-mail: stay@purdue.edu
Search for more papers by this authorSang Eun Woo
Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
Search for more papers by this authorLouis Hickman
Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
Search for more papers by this authorLouis Tay,
Sang Eun Woo,
Louis Hickman,
Rachel M. Saef,
Corresponding Author
Louis Tay
Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
Correspondence to: Louis Tay, Department of Psychological Sciences, Purdue University, 703 Third Street, West Lafayette, IN 47907, USA.
E-mail: stay@purdue.edu
Search for more papers by this authorSang Eun Woo
Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
Search for more papers by this authorLouis Hickman
Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
Search for more papers by this authorLouis Tay and Sang Eun Woo contributed equally to the paper.
Abstract
In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology
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Citing Literature
Special Issue:Behavioral personality science in the age of big data
September/October 2020
Pages 826-844
