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

From a quick clone and line-count, it has:

  235 kLOC .scala
  136 kLOC .java
  22  kLOC .py
  7   kLOC .rs

I also haven't skimmed this code except very superficially, but perhaps you should since you're out there making statements with your Princeton credentials.

(I posted this comment with the heads-up a few minutes after your comment above and then expanded it as you didn't respond.)