Let's take Stack Overflow as an example. Jeff found a small group of experts and expanded it. They seeded both questions and answers. They didn't bring on just one or two experts though, they brought on enough to ensure a good distribution (not perfect) of viewpoints and then reviewed before expansion. They kept repeating this and didn't optimize for just one kind of developer (Django over Java.) All segments of developers tended to need the same features, but it would show up with one segment first. Getting answers on some topics wasn't possible until the product was more mature. Kill crap ruthlessly like SO did with downvoting and moderator-led deletion.

If you are building an ML model then you are going to need to find a range of experts and either seed from what they are sharing, or create a review system. You can reward people with kudos on a contribution page, donations to open source projects or charities (even on behalf of a group of them), or find another way to motivate them. It just needs some hustle, but you've got to forget about purity, be open about how your model works, and iterate.