Don’t let the python fool you. It is not simple python. I’ll try to add some comments later on to make it more clear what’s going on.
For tuning parameters, it was complicated. Mostly, I did spot-checks on random users at different ranking levels, to try to check that the boundary for banning matched up pretty well with what I thought was the boundary of an acceptable level of jerkishness. That, combined with deeper dives into which comments had made what contributions to the user’s overall rankings. And then talking with existing moderators, looking over the banlists, and bringing up users where they thought the bot was getting it wrong. There were a lot of corner cases and fixes to the parameters to fix the corner cases. Sometimes it was increasing SMOOTHING_FACTOR to make users more equal in rank with each other, when we found some user that was banned because of one bad interaction with some high-rank person who downvoted them. Sometimes it was changing parameters to change how easy it is to overcome a few negatively-ranked postings by being generally positive with the rest of your postings. There are always users for which the right answer is a matter for debate or opinion, but as long as the bot isn’t making decisions that are clearly wrong, I think it’s doing pretty well.
You can look over some places where I talked with people about the bot’s opinion of their user, in this post and this post. I don’t want to publicly do those breakdowns for people who haven’t agreed to have it done to them, but that might give you an idea of how the tuning went. What I did to tune the parameters was the same type of thing as I showed in those comments, just a whole lot more of it.
auk@slrpnk.net 2 months ago
I added an explanation of the details of how it works to the source file that implements the main rank algorithm. The math behind it is not simple, but it’s also not rocket science, if you have some data science abilities and want to check it out.