For any social network, not just a federated one.
My thoughts: The way it works in big tech social networks is like this:
- **The organic methods: **
- your followee shares something from a poster you don’t follow
- someone you don’t follow comments on a post from someone you follow
- you join a group or community and find others you currently don’t follow
- The recommendation engine methods: content you do not follow shows up, and you are likely to engage in it based on statistical models. Big tech is pushing this more and more.
- Search: you specifically attempt to find what you’re looking for through some search capability. Big tech is pushing against this more and more.
In my opinion, the fediverse covers #1 well already. But #1 has a bubble effect. Your followees are less likely to share something very drastically different from what you already have.
The fediverse is principally opposed to #2, at least the way it is done in big tech. But maybe some variation of it could be done well.
#3 is a big weakness for fediverse. But I am curious how it would ideally manifest. Would it be full text search? Semantic search? Or something with more machine learning?
Which is the point I’m trying to make: right now, you cannot use search as a discoverability medium, unless you’re on something the scale of mastodon.social.
Search with a focus on new content discoverability is utterly useless for smaller or single user instances, because a search that only finds things you already know about isn’t exactly a useful search for discoverability.
If I have to be on the biggest instances, then there’s very little difference between something like Bluesky and Mastodon in terms of usability, and uh, I might as well pick the one that’s more likely to have the most growth and diversity of content.
I agree, and it’s why I’ve pretty much migrated back to centralized services with the exception of Lemmy, because Lemmy works very well in terms of finding useful shit to follow in a way that literally no other federated platform does.