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One of the most expensive parts of maintaining a modern information-sharingplatform (e.g., web search, social network) is the task ofcontent-moderation-at-scale. Content moderation is the binary task ofdetermining whether or not a given user-created message meets the editorialteam's content guidelines for the site. The challenge is that the number ofmessages to check scales with the number of users, which is much larger thanthe number of moderator-employees working for the given platform.  We show how content moderation can be achieved significantly more cheaplythan before, in the special case where all messages are public, by effectivelyplatformizing the task of content moderation. Our approach is to use ahub-and-spoke model. The hub is the core editorial team delegated by themanagement of the given platform. The spokes are the individual users. Theratings of the editorial team create the labels for a statistical learningalgorithm, while the ratings of the users are used as features.  We have implemented a primitive version of this algorithm into ouropen-source DimensionRank code base, found at "thinkdifferentagain.art".

A Hub-and-Spoke Model for Content-Moderation-at-Scale on an Information-Sharing Platform