| Context: I teach at Princeton and study social media and recommendation systems. From a very quick skim of the repositories, this appears to be quite limited transparency. The documentation gives a decent high-level overview of how Tweet recommendation works—no surprises—and the code tracks that roadmap. Those are meaningful positive steps. But the underlying policies and models are almost entirely missing (there are a couple valuable components in [1]). Without those, we can't evaluate the behavior and possible effects of "the algorithm." [1] https://github.com/twitter/the-algorithm-ml |
I am assuming that open sourcing the code aims to increase transparency about the business logic of the ranking decisions. At the same time you don't want spammers to be able to easily run experiments against a cloned version of your system.