I can think of a few commercial use cases where this could be incredibly useful but would likely based on the methodology you’ve used. Guess I’m interested in how do you define quality and what attributes are you predicting.
A mood or a persona is really an algorithm representing a batch of those. A lot of them you can get pretty close to just by using the Power Filter directly. But there is some secret sauce.
But broadly speaking, you can think of a Hacker persona as being someone interested in Science and Tech and Education as key broad topic areas, wanting more serious content, and wanting content from credible science-based sources. They prioritise useful content, with more in-depth coverage.
An Activist might be someone with a center-left to left stance, interested in social fields, politics and psychology, but also influenced by trends and what is currently generating a lot of "heat".
If you have representations of what good quality content "within that community of personas" looks like, then you can train models and test scoring systems that try to capture them.
Content quality is a big topic. But our working thesis is that there are canonical examples with domains of expertise of what great content looks like - PG's essays, Pulitzer Prize Winning Journalism, award winning research papers. And there are similar examples of what very bad content looks like - hate speech, extremism etc.
So that's been the broad basis for the models that underly the predictions.
[edits for typos]
I'd love to do an arxiv.org paper or mlconf presentation down the track, as I think we have some interesting work here and I have an academic interest in the field, but it's way too early. In essence, we're working this out as we go to "make the thing" as practitioners first based on some unique insights, rather than it being a product derived from research first.
[Edits for typos/clarity]