Twitter code behind For You tracked authors as either “republican”, “democrat”, “power user”, or “elon” to keep algo tweaks from harming any one group
Musk said he had no idea it was doing that! — Twitter has just released …
MashableMatt Binder
Context & Ripple Effects
Twitter’s partial release of its recommendation code created an unusual window into how the For You feed was evaluated, while leaving advertising recommendations outside the release. The disclosed labels show that the system’s testing framework treated political affiliation, high-activity users, and Musk-related accounts as groups whose outcomes could be monitored.
That makes the release more than a transparency gesture: it exposes a governance question about who defines protected cohorts in ranking changes and how closely leadership understands those choices. The earlier partial opening of For You’s code made this level of inspection possible.
First-order effects
Twitter’s engineering and product teams face immediate scrutiny over the use of “republican,” “democrat,” “power user,” and “elon” author labels in safeguards for algorithm changes.
Musk’s stated lack of awareness heightens questions about internal oversight of ranking-policy implementation, even where the stated purpose was to prevent one group being disproportionately harmed.
Second-order effects
Researchers, users, and political observers can use the code disclosure to test Twitter’s public claims about feed neutrality against the cohorts its systems explicitly monitor.
The episode raises the bar for any platform that publishes recommendation code: disclosures can reveal not only ranking mechanics, but also the internal categories used to evaluate distributional effects.
Third-order effects
If platforms increasingly expose recommendation systems, debate will shift from whether algorithms are public to whether their evaluation cohorts, objectives, and decision ownership are legible and accountable.
The durable tension is that safeguards against uneven algorithmic impact require grouping users, while the choice of groups can itself become a source of political and governance controversy.
The trend: This is one data point in the shift from opaque feed ranking toward contested transparency over the metrics and user cohorts that govern algorithmic changes.
PS: People saying, “Meh, the module is just for collecting data on whether various A/B feature changes end up being good or bad for Elon personally” do not seem to understand how very entertaining it is to see Elon inserting “Benefit to Me” as an evaluation metric.
I asked Elon Musk why his name is hard coded here in Twitter Algorithm repo; you can listen to the answer yourself... and the Space finished right after that 🤭 https://github.com/... https://twitter.com/...
Part of Twitter's algorithm specifically designates Jack Dorsey, Katy Perry, Stephen Curry and Barack Obama as “testing accounts” for getting random Tweets for testing, with an emphasis on Katy Perry in particular https://github.com/... https://twitter.com/...
Unless the Democrat/Republican tags are assigned only to elected officials... wtf does this mean? Are the scrapping voter registration databases and cross referencing? Vibes? What? https://twitter.com/...
elon was asked about this on Twitter Spaces and he said “this is the first time i'm seeing this” and said they should change it he also decided that the person who asked him about it would be the last speaker and ended the Space right after https://twitter.com/...
Told you 🤦♂️ This is because legacy checkmarks get a low weight and the system needed a way to differentiate Elon Musk since he has the same legacy checkmark ✅ https://twitter.com/...
It's even funnier to think they tried to remove all the places where Elon had been hard coded into the Twitter algorithm before releasing the code but missed one. $44B to become the most popular user on Twitter🤦🏾♂ ️https://twitter.com/... https://t.co/XdR8GhGesD