Wrong but useful. Working @Twitter, tweeting about data science and people management.
"Wait, it's all her views?" 
"Always has been"
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Calibrating something like this to your specific community or platform is not trivial, and even once you have calibrated the model, there’s the question of what you do with its output once you’ve got it. Is it automatically actioned? Reviewed by a human?
Then you have to measure the impact of your policy, a task which is a sprawling nightmare in and of itself. Do you focus on business impact, the effect on your users? How do you evaluate the impact of someone who made an off-color joke vs. someone who’s a continual net negative?
Obviously, there aren’t a lot of standards or best practices around this yet. We wouldn’t be having controversies like the ones mentioned in this article if we did.
As people continue to live more of their lives online, strewing UGC in their wake wherever they go, the conversation about moderation standards will be forced. I’ll be curious what kind of winding road it ends up taking
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