BCC’ing AI: Using Modern Natural Language Processing to Detect Micro and Macro E-ggressions in Workplace Emails

Subtle offensive statements in workplace emails, which I term "Micro E-ggressions," can significantly impact the psychological safety and subsequent productivity of work environments despite their often-ambiguous intent. This thesis investigates the prevalence and nature of both micro and macro e-ggressions within workplace email communications, utilizing state-of-the-art natural language processing (NLP) techniques. Leveraging a large dataset of workplace emails, the study aims to detect and analyze these subtle offenses, exploring their themes and the contextual factors that facilitate their occurrence. The research identifies common types of micro e-ggressions, such as questioning competence and work ethic, and examines the responses to these offenses. Results indicate a high prevalence of offensive content in workplace emails and reveal distinct thematic elements that contribute to the perpetuation of workplace incivility. The findings underscore the potential for NLP tools to bridge gaps in awareness and sensitivity, ultimately contributing to more inclusive and respectful workplace cultures.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Psychology

Department dc:contributor.department

Psychology

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Cornett, Kelsi E.

Chair dc:contributor.committeechair

• Hernandez, Ivan

Committee members dc:contributor.committeemember

• Ward Bartlett, Anna Katherine
• Calderwood, Charles

• Microaggressions
• Workplace mistreatment
• Natural language processing
• Diversity

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10919/120627

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/120627