Experiences of interpersonal racism persist as a prevalent reality for BIPOC (Black, Indigenous, People of Color) in the United States. One form of racism that often goes unnoticed is racial microaggressions. These are subtle acts of racism that leave victims questioning the intent of the aggressor. The line of offense is often unclear, as these acts are disguised through humor or seemingly harmless intentions. In this study, we analyze the language used in online racial microaggressions ("Acts") and compare it to personal narratives recounting experiences of such aggressions ("Recalls") by Black social media users. We curated a corpus of acts and recalls from social media discussions on platforms like Reddit and Tumblr. Additionally, we collaborated with Black participants in a workshop to hand-annotate and verify the corpus. Using natural language processing techniques and qualitative analysis, we examine the language underlying acts and recalls of racial microaggressions. Our goal is to understand the lexical patterns that differentiate the two in the context of racism in the U.S. Our findings indicate that neural language models can accurately classify acts and recalls, revealing contextual words that associate Blacks with objects that perpetuate negative stereotypes. We also observe overlapping linguistic signatures between acts and recalls, serving different purposes, which have implications for current challenges in social media content moderation systems. / Master of Science / Racial Microaggressions are expressions of human biases that are subtly disguised. The differences in language and themes used in instances of Racial Microaggressions ("Acts") and the discussions addressing them ("Recalls") on online communities have made it difficult for researchers to automatically quantify and extract these differences. In this study, we introduce a tool that can effectively distinguish acts and recalls of microaggressions. We utilize Natural Language Processing techniques to classify and identify key distinctions in language usage and themes. Additionally, we employ qualitative methods and engage in workshop discussions with Black participants to interpret the classification results. Our findings reveal common linguistic patterns between acts and recalls that serve opposing purposes. Acts tend to stereotype and degrade Black people, while recalls seek to portray their discomfort and seek validation for their experiences. These findings highlight why recalls are often considered toxic in online communities. This also represents an initial step towards creating a socio-technical system that safeguards the experiences of racial minority groups.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115745 |
| Date | 11 July 2023 |
| Creators | Gunturi, Uma Sushmitha |
| Contributors | Computer Science and Applications, Rho, Ha Rim, Fox, Edward A., Lourentzou, Ismini |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf, application/pdf |
| Rights | Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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