The emboldening of white supremacist groups, as well as their increased mainstream presence in online circles, necessitates the creation of studies that dissect their tactics and rhetoric, while offering platform-specific insights. This study seeks to address these needs by analyzing white supremacist content and framing devices on the video hosting website, YouTube. Data were collected through a multi-stage sampling technique, designed to capture a 'snapshot' of white supremacist content on the platform during a 45-day period in 2019. After line-by-line coding and qualitative thematic analysis, results showed that sampled channels varied between different levels of color-blindness and overt racialization in their framing. Furthermore, channels containing more color-blind approaches yielded higher subscriber counts than their counterparts. What this indicates is that sampled channels use framing to both activate racial threat and minimize race, attempting to reproduce racism while avoiding coming off as racist in the color-blind, mainstream political climate. Secondary findings also show how sampled channels (a) rhetorically bridge the gap between fascism, nationalism, hegemonic gender roles, and mainstream conservative thought; (b) reconcile the idea of political action within a perilous and conspiratorial worldview; (c) leverage interactive, visual media to engage, manage, and collect funding from their audiences. This study is unique because it unpacks the discursive intricacies of white supremacist messaging, while showing the processes by which a racist society is reproduced in the cosmopolitan, digital hub that is YouTube. It sets precedent and opens doors for future inquiry into how social media platforms are used as tools to mainstream white supremacist ideas.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1026 |
| Date | 01 January 2020 |
| Creators | Charles, Christopher |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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