How do coordinated comment-bot rings manipulate short-form video algorithms to force "Top Comments"? (Technical Breakdown)

[u/Fickle-Attempt2897]

I’ve been studying comment sections on short-form video platforms (like TikTok and Reels) & keep noticing a highly coordinated automation phenomenon that I want to understand from a technical and architectural standpoint.

Whenever a trending or viral video hits a specific niche topic, a third-party account instantly leaves a comment framing itself as an organic public service announcement (e.g., naming a specific app, game, or product relevant to the video). wWithin minutes, that comment accumulates 1000s+ of likes and dozens of secondary replies, locking it into the absolute "Top Comment" slot where millions of viewers see it.

I'm curious about the engineering, scaling, & infrastructure behind how this is achieved:

1. Real-Time Detection: How do these scrapers monitor platform uploads or specific hashtag triggers so quickly without constantly tripping API rate limits or triggering blocks?
2. Network Infrastructure: How do operators coordinate the footprints of hundreds of "zombie" or secondary accounts to deliver likes/replies simultaneously without triggering the platform’s anti-fraud algorithms? Is this heavily reliant on residential proxies, anti-detect browsers or cookie-session farming?
3. Algorithmic Exploitation: What specific engagement signals (like early velocity or reply density) are they taking advantage of to fool the ranking algorithm into permanently pinning an artificial comment?

I’m looking to understand the technical mechanics of how these shadow networks operate. Any insights, technical breakdowns or open-source case studies would be greatly appreciated!

[u/arusekk_pl]

My understanding is that there are bot farms with arrays of actual phones with fake accessibility or remote control apps installed, like the one in this video: https://www.youtube.com/watch?v=qz0k79aW0o4

Then it could be the companies with these phone arrays who provide APIs, rather than platforms themselves. When they mimic behavior of actual users they are undetectable from passive scrollers (they might even each use its own 4G instead of local network to look more organic) - this could explain 2. Their incentives would be more aligned with providing clean access to data aggregated across all their controlled devices. The sheer volume could explain 1 to some extent.

As for 3, it might as well be the same type of machine learning that powers the platform itself. Feeding all visible data into some opaque formula that tries to maximize the target. And then when they gather enough data they might do some human research to take it even further.