Baking Gender Into Social Media Design: How Platforms Shape Categories for Users and Advertisers
Bibliographic record
Abstract
In recent years, several popular social media platforms have launched freeform custom gender fields. This decision reconstitutes gender categories beyond an oppressive binary only permitting “males” and “females.” In this work, we uncover many different user-facing gender category design strategies within the social media ecosystem, ranging from custom gender options (on Facebook, Google+, and Pinterest) to the absence of gender fields entirely (on Twitter and LinkedIn). To explore how gender is baked into platform design, this article investigates the 10 most popular English-speaking social media platforms by performing recorded walkthroughs from two different subject positions: (1) a new user registering an account, and (2) a new advertiser creating an ad. We explore several different spaces in social media software where designers commonly program gender—sign-up pages, profile pages, and advertising portals—to consider (1) how gender is made durable through social media design, and (2) the shifting composition of the category of gender within the social media ecosystem more broadly. Through this investigation, we question how these categorizations attribute meaning to gender as they materialize in different software spaces, along with the recursive implications for society. Ultimately, our analysis reveals how social media platforms act as intermediaries within the larger ecosystem of advertising and web analytics companies. We argue that this intermediary role entrusts social media platforms with a considerable degree of control over the generation of broader categorization systems, which can be wielded to shape the perceived needs and desires of both users and advertising clients.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.006 |
| 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".