Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining. Issue 5 (16th July 2020)

• Record Type: Journal Article
• Authors: Tay, Louis; Woo, Sang Eun; Hickman, Louis; Saef, Rachel M.
• Editors: Rauthmann, John
• Other Names: Rauthmann John F. guestEditor.
• Is Part Of: European journal of personality. Volume 34:Issue 5(2020)
• Page Start: 826
• Page End: 844
• Publication Date: 2020-07-16
• Subjects: machine learning -- big data -- social media -- text mining -- validity; Personality -- Periodicals; 155.2
• DOI: 10.1002/per.2290
• Languages: English
• ISSNs: 0890-2070

Abstract

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