A free market in extreme speech: Scientific racism and bloodsports on YouTube
Bibliographic record
Abstract
Abstract Around 2018, YouTube became heavily criticized for its radicalizing function by allowing far-right actors to produce hateful videos that were in turn amplified through algorithmic recommendations. Against this ‘algorithmic radicalization’ hypothesis, Munger and Phillips (2019, A supply and demand framework for YouTube politics. Preprint. https://osf.io/73jys/download; Munger and Phillips, 2020, Right-wing YouTube: a supply and demand perspective. The International Journal of Press/Politics, 21(2). doi: 10.1177/1940161220964767.)) argued that far-right radical content on YouTube fed into audience demand, suggesting researchers adopt a ‘supply and demand’ framework. Navigating this debate, our article deploys novel methods for examining radicalization in the language of far-right pundits and their audiences within YouTube’s so-called ‘Alternative Influence Network’ (Lewis, 2018, Alternative Influence. Data & Society Research Institute. https://datasociety.net/library/alternative-influence/ (accessed 9 December 2020).). To that end, we operationalize the concept ‘extreme speech’—developed to account for ‘the inherent ambiguity of speech contexts’ online (Pohjonen and Udupa, 2017, Extreme speech online: an anthropological critique of hate speech debates. International Journal of Communication, 11: 1173–91)—to an analysis of a right-wing ‘Bloodsports’ debate subculture that thrived on the platform at the time. Highlighting the topic of ‘race realism’, we develop a novel mixed-methods approach: repurposing the far-right website Metapedia as a corpus to detect unique terms related to the issue. We use this corpus to analyze the transcripts and comments from an archive of 950 right-wing channels, collected from 2008 until 2018. In line with Munger and Phillips’ framework, our empirical study identifies a market for extreme speech on the platform, which came into public view in 2017.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".