Huh? The guy with the 10 year post history called Subverter a shill. Subverter has a 43 day post history, three anti-net neutrality comments, and two anti-net neutrality submissions. You can't see one, because it was flagged:
The reason I have a 43 day post history is because that's how long my site of the same name has been in existence. I've been a long-time reader of HN and commented (albeit rarely) under my own name for many years.
The reason many of my comments and submissions have been net neutrality related is because it's exactly the type of topic that my site deals with: technology and freedom. In addition to privacy, free speech & censorship, cryptocurrency & blockchain, etc.
I find this “shillcalling” very annoying, but I understand where it’s coming from. If you look at Reddit, for example, it has been an entire two weeks of constant “Bill Gates is great” posts. Astroturfing is out of control.
At the same time it makes discussion impossible. I have myself been called a “shill” on several occasions now (not here), just by disagreeing with the narrative slightly. Any dissent is now seen with suspicion which just pushes everyone more and more to the extremes.
Since you made a post about machine learning assisted moderation, I am wondering if you thought about how ML could be used to bring back discussion to a neutral, or at least merit-based scoring?
It seems everyone wants to apply ML to the content itself (like Perspective), but wouldn’t it be more effective to instead use ML to assign weight to the ratings? For every topic there must exist a subset of people that can judge it by its merit, without immediately succumbing to their biases. It seems if you could identify that subset of users, and weight their scoring more (and that subset might be different per topic), then it should be possible to avoid the usual traps of popularity-based scoring systems, including astroturfing and vote manipulation.
What do you think?