Couple problems:

1. Actively supplying negative feedback is sometimes hidden behind secondary menus, making it much higher friction compared to just...scrolling past. So most users don't spend the effort. Even with a dislike button, it's unclear what the system is learning. It can't know that I don't like this particular video because it's a conspiracy theory, and to stop showing me those. These platforms often don't even support explicit categories, so how would they know?

2. It's extremely high friction to teach the algorithm you're interested in something that it doesn't suggest to you! There's the whole unknown unknowns problem: how do you teach the algorithm you're interested in something that you've never seen before?

I still think Reddit has handled this the best. No system is perfect, but Reddit's challenges are much more manageable than the quagmire that TikTok, Facebook, and YouTube have gotten themselves into. I can just unsubscribe from r/conspiracy, and I'm out. Basically impossible to teach that to YouTube without weeks of careful curation. They think they're smart enough to know what I like, but they're not and never will be.

Twitter could recreate a similar system: 1) auto-tag tweets with labels, rather than users needing to submit to subreddits 2) auto-sub most people some default set of labels 3) let them un-sub if they want.

They just don't want to.

Auto-tagging is not a business they want to be in, because they will invariably get it wrong sometimes and people will start drama over it. And they'd be right to: human communication is nuanced and you can't just "guess" what someone means within 280 characters.

People already use hashtags, what would happen if you could subscribe to them? It would create a whole new way of interacting with the platform, something closer to Reddit where people post to streams. But the fact that these streams can't be moderated (or even downvoted) might be a nonstarter.

Wait, Twitter already has this in the form of Topics. It's very coarse-grained, but still.

Regarding 1. I would expect like rate on recommended tweets to be a better signal than impressions.

Eh, 1 is sort of why I see implicit negative feedback as more useful here. Namely, tracking the duration a user is probably giving their attention to a given item, weighted in accordance with how long you'd expect someone to give their attention to an item based on how long it is.

For example, I might see a some specific word or pattern of words in a tweet and quickly skip to the next one. That's very low friction but also a powerful signal. There are drawbacks (e.g. the long boring video that looks like something is about to happen but never does), but that's mitigated by combining this with other signals.

With 2, it's an explore/exploit trade-off. You try to explore as far and wide as you can while trying to avoid the things the user may dislike, all while sprinkling in just enough of the stuff that you know they'll like.

You're talking like an ML engineer. :)

Yes, there are signals in human behavior that you can feed to your model. But no, it is never going to learn "on Mondays he works on his comics, so he'd prefer to see webcomic-related content" Don't sprinkle in your explore/exploit experiments. I know what I want, just let me decide based on what I'm in the mood for!

TikTok-style feeds are the absolute worst offender here, where they couldn't care less about what you think. They will serve you content, and you will either say "yes" or "no". So the only option for you as the user is to just wander through their content hyperspace. There's no structured way to jump between topics because everything lives in this formless content soup.

The other problem is that many social media platforms have you subscribe to the content streams of individuals directly. Individuals are high variance. How can you teach these engines that "I only care about this person's posts about pianos, not their terrarium hobby."

> Namely, tracking the duration a user is probably giving their attention to a given item,

I pay a lot of attention to classes of things I don't want to see at all.

Then, according to the algorithm, you do want to see them.

That's exactly the problem! I don't want to see them, but the algorithm refuses to ask me.

If they think that the length of time I spend on a post is 100% correlated with the amount that I want to see it, they really don't understand people at all.

> It's an embarrassingly shallow model of how how humans behave.

It's a trade off. Viewing rates give back feedback 100% of the time. Asking users for a thumbs up or down gives feedback almost none of the time, and still might not be accurate.