In this paper, we present a digital tool named (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 LHGBTQ+ 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 ( = 84,561) of large organizations in the Netherlands. Our results show that in this context gender diversity is most prevalent, followed by LHGBTQ+ and cultural/ethnic/racial diversity. Furthermore, gender diversity is often associated with the innovation perspective, whereas LHGBTQ+ 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.

中文翻译：

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使用监督机器学习检查社交媒体上的战略多样性传播：开发、验证和未来研究方向

在本文中，我们提出了一种名为（DivPSM）的数字工具，它使用监督机器学习对组织社交媒体帖子中的战略多样性沟通进行自动内容分析。DivPSM 经过训练，可识别帖子是否提及多样性或与多样性相关的问题，并随后对三个多样性维度（文化/民族/种族、性别和 LHGBTQ+ 多样性）和三个多样性视角（道德、市场和创新观点）。在研究 1 中，我们描述了仪器的训练和验证，并检查了它与人类编码员相比的表现。我们的研究结果证实 DivPSM 足够可靠，可用于未来的研究。在研究 2 中，我们通过分析荷兰大型组织的社交媒体帖子 (= 84,561) 中战略多样性沟通的流行程度，说明了 DivPSM 生成的数据类型。我们的结果表明，在这种情况下，性别多样性最为普遍，其次是 LHGBTQ+ 和文化/民族/种族多样性。此外，性别多样性往往与创新视角相关，而 LHGBTQ+ 多样性则更多地与道德视角相关。文化/民族/种族多样性与任何观点都没有表现出强烈的关联。本文最后讨论了未来研究的理论意义和方向。