How Facebook Uses AI to Moderate Content

Content flows from upload to decision. From raw posts to keep or remove. The AI decides what stays and what goes.

Facebook handles billions of pieces of content each day. The moderation system catches over 90% of bad content before users report it. The platform still allows users to report content that slips through. According to reports on Facebook's AI approach, this works through layers of AI tools. Hash-matching finds known bad content. Text classifiers read new posts. Large Language Models check the final call.

Here's how the AI content moderation works.

Key takeaways

• Hash-matching checks uploads against 784,000+ known content violations for fast removal
• Text classifiers like DeepText and XLM-R read posts in 100 languages using machine learning
• The Rosetta system pulls text from over 1 billion images daily to moderate content in memes
• Large Language Models give a second opinion before removal with 0.939 accuracy scores
• Content routes by confidence where clear violations get auto-removal and unclear cases go to review teams

Content types that need review

Facebook runs across many platforms. Users post many types of content that need checks against community standards.

Text posts

Users write status updates and comments. This includes posts in over 100 languages. Text comes in a format that AI systems can read right away.

Images and videos

Photos and videos need different tools than text. Users share memes, live streams, and clips. Some images have text on them that needs to be pulled out first.

Mixed posts

Many posts mix text and images. A single post might have words, a meme, and links. The system must check all parts together.

You want to know these types because each needs its own AI models. The goal is sending each piece to the right tool.

Hash-matching layer

The first layer uses hashing to catch known bad content fast. Facebook's official content review process starts here.

How it works

Hashing makes digital fingerprints of content. These fingerprints spot content even when changed a bit. A cropped image or smaller video still matches.

PDQ handles images. TMK+PDQF handles videos. Tech companies share these fingerprints through groups like the Global Internet Forum.

The database

The system checks uploads against 784,000+ known harmful videos. When a match hits, removal takes seconds. This catches content that was flagged before.

Hash-matching works fast because it runs before any deeper review. Matches get removed without more analysis.

Text classification layer

For new content that hashing misses, classifiers read the meaning. Research on how platforms use AI for moderation shows text analysis forms the core of this layer.

DeepText

DeepText uses neural networks to grasp post meaning. It looks at sentence structure and word links. It sorts content based on patterns from past examples.

XLM-R

XLM-R is a model trained on 2.5TB of data across 100 languages. This lets the same AI models handle English, Spanish, Arabic, and more. The model learns patterns that show policy breaks.

Rosetta

Rosetta reads text from over 1 billion images daily. It pulls words from memes and screenshots. Once pulled, this text flows through the same classifiers as normal posts.

These moderation tools work as a team to review content at scale.

Few-Shot Learner

Old classifiers need hundreds of thousands of examples. The Few-Shot Learner needs far less.

Learning fast

This system learns new rules from just dozens of examples. When Facebook adds new community standards, the Few-Shot Learner can start in weeks instead of months.

Quick deployment

Past AI systems took six months to set up for new violation types. The Few-Shot Learner cuts this to six weeks. Speed matters when new harms pop up.

Large Language Model layer

The newest part of how Facebook uses AI to moderate content uses LLMs for oversight. The Oversight Board notes this marks a new era for AI and automation in content decisions.

Llama Guard

Llama Guard 3 scores 0.939 on English content tests. This means AI models now beat humans for some policy areas. It cuts false positives in half.

Second opinion

LLMs now check decisions before posts get removed. This catches errors that simpler tools make on tricky hate speech cases. Critics argue the underlying algorithm raises concerns beyond moderation accuracy.

The small Llama Guard version runs at just 440MB. This allows on-device checks before content even uploads.

Confidence routing

The system routes content based on how sure the AI feels.

High confidence

When AI shows high confidence of a violation, auto-removal happens. No human review teams see it. This handles clear cases like known terrorism content.

Medium confidence

Content where AI feels unsure goes to human queues. These queues sort by:

• How fast the content spreads
• How bad the potential harm is
• Whether users report it

Low confidence

Content with low violation odds stays up. It only gets removed if users report it and review confirms a problem. Studies on how automated moderation works show this tiered approach is common across platforms.

This tiered setup balances speed with care. Clear violations get fast action. Edge cases get human judgment.

Technical summary

This flow shows how artificial intelligence handles content at Facebook scale:

Hash-matching:

PDQ and TMK+PDQF check against known violation databases

Text classification:

DeepText, XLM-R, and Rosetta read novel content

Adaptation:

Few-Shot Learner enables quick setup for new policy types

Verification:

Llama Guard gives LLM oversight before removal

Routing:

Confidence scores set auto-removal versus human review

The layered setup ensures each piece flows through the right automated systems based on type and certainty.

FAQ

What triggers auto-removal?

High confidence scores from AI or hash matches against known bad content. The system removes without human review when certainty passes the threshold.

How do AI models learn the rules?

Machine learning trains on labeled examples. Human reviewers mark content as good or bad. Models learn patterns from these examples and apply them to new posts.

Why does accuracy vary by language?

XLM-R trains on 100 languages but with unequal data. Languages with more examples produce better results. Less common languages get fewer examples to learn from.

How fast does it work?

Hash-matching takes seconds. Text classification adds time based on complexity. Most content gets a decision before it shows in feeds.

Can users appeal?

Yes. Users can ask for human review of removals. Review teams look at appeals and can restore wrongly removed content.

Summary

Facebook's AI moderation works through layers that each handle part of the job. Hash-matching catches known violations fast. Text classifiers read new content across languages. LLMs verify decisions before action.

The routing system balances automation with human checks. Clear violations get auto action. Unclear cases go to review teams.

This setup enables moderation at a scale humans alone could not reach. The role of AI in improving content moderation continues to grow. Billions of pieces of content get sorted daily through systems that keep learning.

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