Queer women’s experiences of patchwork platform governance on Tinder, Instagram, and Vine
Bibliographic record
Abstract
Leaked documents, press coverage, and user protests have increasingly drawn attention to social media platforms’ seemingly contradictory governance practices. We investigate the governance approaches of Tinder, Instagram, and Vine through detailed analyses of each platform, using the ‘walkthrough method’ (Light, Burgess, and Duguay, 2016 The walkthrough method: An approach to the study of apps. New Media & Society 20(3).), as well as interviews with their queer female users. Across these three platforms, we identify a common approach we call ‘patchwork platform governance’: one that relies on formal policies and content moderation mechanisms but pays little attention to dominant platform technocultures (including both developer cultures and cultures of use) and their sustaining architectures. Our analysis of these platforms and reported user experiences shows that formal governance measures like Terms of Service and flagging mechanisms did not protect users from harassment, discrimination, and censorship. Key components of the platforms’ architectures, including cross-platform connectivity, hashtag filtering, and algorithmic recommendation systems, reinforced these technocultures. This significantly limited queer women’s ability to participate and be visible on these platforms, as they often self-censored to avoid harassment, reduced the scope of their activities, or left the platform altogether. Based on these findings, we argue that there is a need for platforms to take more systematic approaches to governance that comprehensively consider the role of a platform’s architecture in shaping and sustaining dominant technocultures.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".