BCC’ing AI: Using Modern Natural Language Processing to Detect Micro and Macro E-ggressions in Workplace Emails
| dc.contributor.author | Cornett, Kelsi E. | en |
| dc.contributor.committeechair | Hernandez, Ivan | en |
| dc.contributor.committeemember | Ward Bartlett, Anna Katherine | en |
| dc.contributor.committeemember | Calderwood, Charles | en |
| dc.contributor.department | Psychology | en |
| dc.date.accessioned | 2024-07-10T12:20:59Z | en |
| dc.date.available | 2024-07-10T12:20:59Z | en |
| dc.date.issued | 2024-05-24 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | 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. The results show a high occurrence of offensive content in workplace emails and highlight patterns that help maintain a negative work environment. The study demonstrates that advanced language analysis tools can help raise awareness and sensitivity, ultimately fostering more inclusive and respectful workplace cultures. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Cornett, K. (2024). BCC’ing AI: Using modern natural language processing to detect micro and macro e-ggressions in workplace emails (Master's thesis). Virginia Polytechnic Institute and State University. | en |
| dc.identifier.uri | https://hdl.handle.net/10919/120627 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Microaggressions | en |
| dc.subject | Workplace mistreatment | en |
| dc.subject | Natural language processing | en |
| dc.subject | Diversity | en |
| dc.title | BCC’ing AI: Using Modern Natural Language Processing to Detect Micro and Macro E-ggressions in Workplace Emails | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Psychology | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |