Design a Content Moderation System
A content moderation system detects user-generated content that violates a platform's policies, such as hate speech, harassment, threats, or explicit material, and decides what to do about it. It combines ML classifiers, rules, and known-content matching with a policy decision layer and human reviewers, so that clear cases are handled automatically and ambiguous ones get human judgment.
Imagine you're building a social media platform where millions of people upload posts, images, comments, and videos every day.
Most of this content is perfectly harmless. But some of it contains harassment, hate speech, threats, scams, graphic violence, or other material that violates the platform's policies.
Now, imagine trying to review every piece of content manually. At a hundred million uploads per day, that's simply not practical. We need machine learning to identify potentially harmful content automatically, take action when we're sufficiently confident, and involve human reviewers when the decision requires more context.
But there's a catch.
If our system is too lenient, harmful content spreads across the platform. If it's too aggressive, we end up removing legitimate content, frustrating creators, and potentially suppressing important conversations.
And some of the most difficult examples are also the most common sources of mistakes. A sentence might contain offensive language while condemning harassment. A violent image might be part of a news report. A sarcastic comment might look like a genuine threat when we remove its context.
So, designing a content moderation system isn't just about training a classifier. It's about making reliable decisions under uncertainty, at massive scale, while balancing safety, accuracy, latency, and fairness.
In this article, we'll approach this problem exactly as we would in an ML system design interview. We'll start by defining the problem, then work through the data, models, evaluation metrics, serving architecture, human review, and production challenges.