Proposing Botwatch

Summary

Proposing BotwatchTrust-Based Community Bot DetectionPaul JohnecheckMarch 23, 2026Is this account a bot? This question's importance is only growing in a world of LLMs and state-backed influence campaigns. Proposed here is Botwatch, a trust-based system for personalized bot detection.In Botwatch, users publish records indicating whether they think others are bots and records indicating trust in a user’s scores. By analyzing this network, we can create useful signals to help users distinguish between bots and humans. Such a signal would consider your trust relations and output a personalized estimated bot score for a target user. There’s an example at the end of this proposal, but you don’t need to read it to know how it should work. If all the people you trust agree that someone is a bot or human, it should agree. If the people you trust have mixed opinions, perhaps the formula should be uncertain. Naturally, misplaced trust will result in inaccurate results. The hope, though, is that with sufficient scores and well-placed trust, these heuristics will correlate with the truth.Users can publish their perspectives on whether other users are bots. They score other users on a scale of -1 to 1 (or, equivalently, -100% to 100%). Users don't need to input these as numbers; an app might present this as a slider or as a few options.Score: Meaning----------------------1: Certainly a bot0.5: Probably a bot0: Uncertain-0.5: Probably not a bot-1: Certainly not a botHere's a few examples:Alice meets Bob at a convention. They exchange information and become certain that each other's accounts represent a real person. They each publish a score of -1 for the other.Charlie encounters @void.comind.network, a bot. Void doesn't pretend to be human. He gives Void a score of 1.A company offers verification services. It will score you -0.85 if you upload your ID and pay a small fee.Dave sees a suspicious account online. It's posting some bad takes and used an em-dash once. Dave rates it 0.9....