Alicia Zumbusch

perfect substack to link to when i post.

Excellent deep dive by prominent academic in how algorithms operate.

Passages of note:

most major platform companies are quite open about discussing their recommendation algorithms at academic and industry conferences and learn from each other. Much of what I wrote above is disclosed in well-known research papers. But it turns out that the details that matter from a research and engineering perspective are subtly different from those that matter to users and to society. And companies seem to have little incentive to be transparent about the algorithm with those stakeholders. That explains the current unsatisfactory and somewhat paradoxical state of algorithmic transparency.

To predict engagement by a given user on a given post, most major recommendation algorithms try to answer the question: How did users similar to this user engage with posts similar to this post? The intuition behind this logic is straightforward: Two people who have something in common—a hometown, a hobby, a community they are embedded in, a celebrity they follow—will both engage with posts that relate to that shared interests.

If a user spends an hour a day on TikTok for four years,41 the average video length is 20 seconds, and they skip half the videos, the platform has interaction records on over half a million videos for that single user.

Long video, very informal, but similar optimistic nihilism