For any social network, not just a federated one.

My thoughts: The way it works in big tech social networks is like this:

  1. **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
  1. 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.
  2. 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?

  • Cyclohexane@lemmy.mlEnglish
    4·
    8 days ago

    I wish we had a nice tagging system (and I don’t think they should be hashtags) that was also in common use.

    I want to be able to search any post related a certain topic, and sometimes, these may not always be in that topic’s community, because topics can overlap. For example, I might want to read posts about Ukraine war, but those might be in world news, US news, or combat footage communities. Could be a community about Ukraine in general, or Ukraine war specifically.

    I also may not want to get it from a single Ukraine community. Maybe by finding posts with the “Ukraine war” tag, I’ll see several communities and join the one I want. But there needs to be a way to group them somehow.

    Such a tag system may be useful for combined topics. For example, I may want to look for posts about music software. They might not be common in the music community, or software communities. But I could filter by both tags and find what I want.