Social networks generally have far more things they could show you than you’ll be able to look at. To prioritize they use inscrutable algorithms that boil down to “we show you the things we predict you’re going to like”.
Presumably, social networks tend to optimize for metrics like time spent and user retention. (There might even be a causal relationship between this optimization and threads getting derailed.)
Also, this seems like a stable/likely state, because if any single social network would unilaterally switch to optimize for ‘showing users things they like’ (or any other metric different from the above), competing social networks would plausibly be “stealing” their users.
Users liking / interacting with things is a strong leading indicator of engagement and time spent, and you get it on a per-item basis. So you use those predictions heavily in deciding what to show people, but tune your model based on your larger scale metrics like time spent.
Presumably, social networks tend to optimize for metrics like time spent and user retention. (There might even be a causal relationship between this optimization and threads getting derailed.)
Also, this seems like a stable/likely state, because if any single social network would unilaterally switch to optimize for ‘showing users things they like’ (or any other metric different from the above), competing social networks would plausibly be “stealing” their users.
Users liking / interacting with things is a strong leading indicator of engagement and time spent, and you get it on a per-item basis. So you use those predictions heavily in deciding what to show people, but tune your model based on your larger scale metrics like time spent.