[ziddoap]

While I am skeptical of what reasonable conclusions can be drawn from a study like this, they explain the methodology in the article. You said:

>Typically [...] followed by random scrolling during which the recommended videos are collected and classified [...] Modern recommendation algorithms famously work by examining how long and how users engage with content, and there's none of that going on here.

But they claim that videos are watched, not just collected from the recommendation page.

"The accounts watched 10 videos, followed by a one-hour pause, and repeated this process for six days"

[xiii1408]

Perhaps I should have been more clear. It's TikTok, so of course the only way to collect recommendations is to watch videos. Some studies watch the whole video, some just watch part of it, but it's TikTok, so fundamentally you're watching a video.

[ziddoap]

I might just not be reading it properly. I've never used TikTok, I assumed by your description that they scraped video titles/transcripts/etc. from the recommendation page without any engagement on the video. (I suppose I should read the study you linked!)

When you say "how users engage with content, and there's none of that going on here", by "none of that", do you just mean likes/comments, that sort of thing?

I would usually consider watching as engaging with content, but if you mean additional engagement (as I would call it, anyways), that would make a lot more sense to me.

[xiii1408]

I think the key metric missing here is how long the user watches each video. Likes and replies are probably helpful too, but when I've used short-form video content apps like TikTok, Reels, and YouTube Shorts before, they've gotten a pretty good measure of me without me ever liking, replying, or following.

With the current methodology, the bot either watches the whole video, a fixed duration of it, or a random duration before swiping. The bot doesn't organically watch or swipe based on its interests like a human user would.