‘Definitely not in the business of wanting to be associated’: Examining public relations in a deplatformization controversy
Bibliographic record
Abstract
In August 2019, a mass shooter in the United States posted a violent manifesto to the anonymous forum 8chan prior to his attack. This was the third such incident that year and afterwards hosting and security services conceded to calls to drop 8chan as a client, pushing 8chan to the margins of the accessible internet. This article examines the deplatforming of 8chan as a public relations crisis, contributing to understanding 'governance by shock' (Ananny and Gillespie 2016) by examining who is shocked and their power to turn shock into online regulation. Online platforms and media attention created opportunities to study how the deplatforming was justified, drawing on the theoretical framework of economies of worth (Boltanski and Thevenot 2006) and controversy mapping methods. The examination finds: (1) that this case of deplatforming indicates the openness of infrastructure-as-a-service companies to external challenges over content, rather than hegemonic control. (2) That regulatory gaps, including the broadness of U.S. free speech laws, made these companies, rather than legal processes, the relevant authority. (3) That framing responsibility as following the law - as Cloudflare attempted to do - misunderstands the importance of normative principles, voluntary measures, and contestation in governing online content, underselling the value of policy-making at other levels. The success of the campaign to deplatform 8chan affirms the significance of PR crises in the regulation of online content, rewarding deplatforming as a political tactic for civil society groups and online networks pushing for governance in regulatory gaps. However, the significance of normative enforcement in this case underlines the difficulties of this semi-voluntary style of governance. While normative opposition to violence contributed to 8chan's deplatforming, other normative oppositions contribute to deplatforming vulnerable users, as in the moral panics that drive the deplatforming of sexual content (Tiidenberg 2021) and feed suspicion over the ideological application of deplatforming. The ambivalence of PR crises as a strategy for influencing platform governance underlines the need for clarity in policy-making at multiple levels.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.046 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| 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".