A creator can be male with a mostly female audience, or the other way around. For campaign fit, the audience's gender split matters more than the creator's own. Audience Gender shows the male / female split of the people who engage with a creator. It's measured on the engaged audience, and it only ships for creators where the sample behind it is large enough to be trustworthy.
Start From the Engaged Audience
For each creator, we analyze a sample of their engaged audience. These are the people a brand actually reaches, which makes them a better base for demographics than raw follower counts. Audience estimates are extrapolated from public engagement signals, and every audience statistic is an aggregate.
The Audience We Analyze
Every audience data point we publish is built on the people who actively interact with a creator's content, because they're the ones who see and respond to campaigns. The full reasoning behind this approach is on the Audience Analytics page.
Gender Detection on Public Signals
The engaged audience goes through the same gender detection that runs across the rest of our data, documented in full on the Gender page. It works from public signals only, and where the signal isn't clear enough, that part of the audience is excluded rather than guessed.
Clear Signal Counts, Weak Signal Doesn't
Not all of an audience carries enough public signal for a gender estimate. That part is simply left out of the calculation. The split is always computed over the share of the audience where the signal was clear, never padded with guesses.
Aggregate With Quality Gates
The estimates are counted into a single male / female split, and the result is only published when the sample is large enough to be trustworthy. If a creator's audience sample is too small, the field stays empty rather than showing a shaky number.
From Estimates to a Split
A split computed from a handful of people is noise, not data. We require the sample behind the field to cover at least 1,000 engagers with a clear signal before publishing, and up to 10,000 for creators with larger audiences. Below that floor, Audience Gender stays empty for that creator. An empty field is a quality signal, not a gap.
Illustrative Example
Two creators, two very different audiences. The creator's own gender tells you neither of these numbers.
Two Creators, Side by Side
A near-balanced audience. Campaigns aimed at either gender reach a meaningful share, and mixed-audience products fit well.
A strongly male audience. A great fit for products aimed at men, and a poor buy for a campaign targeting women, whatever the content looks like.
Both of these creators are male. Only one of them has a male audience. Filtering by creator gender alone would treat them as identical, and this field is what separates them.
Output Format
The exact fields this data point ships with, shown with example values, and how to use them in search.
The Actual Fields
Each value is a percentage of the analyzed sample. The two values sum to 100%.
Filter creators by the share of their audience in either direction, for example "audience female above 60%" for a beauty campaign, or "audience male above 70%" for a men's grooming brand. Combine it with audience age to target a bracket and gender at once.
Validation 
Audience Gender is validated at both levels: the underlying gender detection, and the aggregated splits built from it. Both go through the same two-track process used for every data point.
Getting the underlying gender detection right is not enough. We also validate the final splits, comparing published audience numbers against independently built reference splits for the same creators. This catches errors that only show up in aggregate, and it is why the field ships with a sample floor instead of shipping every creator with any data at all.