Control-Theoretic Content Moderation

2026-09-16

AI Summary

本文通过引入一种基于反馈控制的全局、自适应和序列决策框架来解决平台级内容管理策略问题，该方法在平衡多个竞争目标方面优于局部策略。

Abstract

A sizable literature studies content moderation locally, at the level of individual moderation decisions, for example by measuring or predicting the effects of specific interventions. However, the problem of how such decisions should be combined into effective platform-level moderation strategies is comparatively unexplored. We address this latter problem by formulating content moderation as a global, adaptive, and sequential decision process in which heterogeneous interventions must jointly balance multiple competing objectives. Drawing on feedback control, we introduce a general control-theoretic framework for composing moderation actions according to their expected effects on an evolving platform. We instantiate the framework in large-scale, empirically grounded simulations and compare two control-theoretic moderators against several baselines and local strategies. When moderation aims to maintain competing platform-level properties around desired conditions, the control-theoretic approaches achieve the best overall performance. They also use severe interventions more selectively and recover more effectively after external surges of harmfulness. These results demonstrate the advantages of studying content moderation as a global, adaptive, and sequential decision problem.

Problem

• content moderation
• platform-level strategy
• competing objectives

Innovation

• Control-Theoretic Framework
• Global Adaptive Decision Process
• Content Moderation
• Feedback Control
• Sequential Decision Making