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

TL;DR

This paper introduces a scalable content moderation model for large information-sharing platforms, leveraging a hub-and-spoke system where user ratings inform a machine learning algorithm to reduce moderation costs.

Contribution

The paper proposes a novel hub-and-spoke content moderation framework that effectively utilizes user ratings and a core editorial team to improve scalability and cost-efficiency.

Findings

Achieved significant cost reduction in content moderation.

Implemented a prototype algorithm in open-source code.

Demonstrated effectiveness of user ratings as features.

Abstract

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