Streaming to transgress: the racial politics of reactionary YouTubers and their audiences
Bibliographic record
Abstract
This doctoral thesis examines the racial discourse of “alt-lite” YouTube personalities and their audiences. The term “alt-lite” was coined in the mid-2010s by self-avowed members of the white nationalist “alt-right” movement to castigate fellow reactionaries whose politics broadly aligned with theirs but who were not bold enough to explicitly embrace ethnonationalism. In this thesis, I examine “alt-lite” discourse as a calculated position within the attention economy, one that has been adopted with great success by popular reactionary influencers, particularly on YouTube. Understudied compared to other mainstream social media platforms, YouTube operates as an important launching pad for these right-wing micro-celebrities and serves as the primary field site for this qualitative study. Building on scholarship within critical race and digital studies, cultural studies, and political communication, this thesis asks: What discourses about race circulate within and around “alt-lite” YouTube channels? To answer this question, I draw on two and a half years of online data collection: over 250 YouTube videos; observation of nine Facebook, Reddit, and Discord groups; and semi-structured interviews with 18 current and former viewers of reactionary YouTube channels. I use qualitative content analysis and critical discourse analysis to interrogate these materials and draw conclusions about the strategies and impacts of “alt-lite” influencers. I find that these YouTubers traffic in white supremacist talking points, while adopting rhetorical strategies and legitimating practices that obfuscate their ideological extremity. Even as the most popular “alt-lite” YouTubers bring in substantial salaries from ad revenue, crowdsourcing, subscription fees, and partnerships, they are perceived by audiences as subversive “outsiders,” who are unbeholden to the institutional and ideological constraints of establishment media. Thus, “alt-lite” influencers are emblematic of an “alternative” right-wing media ecology that flourishes online, providing viewers with engaging political commentary that reflects their frustrations, keeps them entertained, and validates their desire to think for themselves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".