Examining strategic diversity communication on social media using supervised machine learning: Development, validation and future research directions
Hofhuis,Joep Gonçalves,João Schafraad,Pytrik Wu,Biyao
Hofhuis,Joep
Gonçalves,João
Schafraad,Pytrik
Wu,Biyao
Abstract
In this paper, we present a digital tool named Diversity Perspectives in Social Media (DivPSM) which conducts automated content analysis of strategic diversity communication in organizational social media posts, using supervised machine-learning. DivPSM is trained to identify whether a post makes mention of diversity or a diversity-related issue, and to subsequently code for the presence of three diversity dimensions (cultural/ethnic/racial, gender, and LGBTQ+ diversity) and three diversity perspectives (the moral, market, and innovation perspectives). In Study 1, we describe the training and validation of the instrument, and examine how it performs compared to human coders. Our findings confirm that DivPSM is sufficiently reliable for use in future research. In study 2, we illustrate the type of data that DivPSM generates, by analyzing the prevalence of strategic diversity communication in social media posts (n = 84,561) of large organizations in the Netherlands. Our results show that in this context gender diversity is most prevalent, followed by LGBTQ+ and cultural/ethnic/racial diversity. Furthermore, gender diversity is often associated with the innovation perspective, whereas LGBTQ+ diversity is more often associated with the moral perspective. Cultural/ethnic/racial diversity does not show strong associations with any of the perspectives. Theoretical implications and directions for future research are discussed at the end of the paper.
Description
Publisher Copyright: © 2024 The Authors
Date
2024-03-01
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
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Journal Issue
Keywords
Computational methods, Diversity, Diversity perspectives, Social media, Strategic communication, Supervised machine-learning
Citation
Hofhuis, J, Gonçalves, J, Schafraad, P & Wu, B 2024, 'Examining strategic diversity communication on social media using supervised machine learning: Development, validation and future research directions', Public Relations Review, vol. 50, no. 1, 102431, pp. 1-10. https://doi.org/10.1016/j.pubrev.2024.102431
License
info:eu-repo/semantics/openAccess