Expressing and Challenging Racist Discourse on Facebook: How Social Media Weaken the “Spiral of Silence” Theory
Bibliographic record
Abstract
This article examines the discursive practices of Facebook users who use the platform to express racist views. We analyzed 51,991 public comments posted to 119 news stories about race, racism, or ethnicity on the Canadian Broadcasting Corporation News Facebook page. We examined whether users who hold racist viewpoints (the vocal minority) are less likely to express views that go against the majority view for fear of social isolation. According to the “spiral of silence” theory, the vocal minority would presumably fear this isolation effect. However, our analysis shows that on Facebook, a predominantly nonanonymous and moderated platform, the vocal minority are comfortable expressing unpopular views, questioning the explanatory power of this popular theory in the online context. Based on automated analysis of 8,636 comments, we found 64 percent mentioned race or ethnicity, and 18 percent exhibited some form of othering. A manual coding of 1,161 comments showed that 18 percent exhibited some form of othering, and 25 percent countered the racist discourse. In sum, while Facebook provides space to express racist discourse, users also turn to this platform to counter the hateful narratives.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".