Content moderation: What it is, how it works, and the best APIs
This article will look at what content moderation is, how it works, some of the best APIs for performing content moderation, and a few of its top use cases.
In 2017, several major brands were up in arms when they found their advertising content had been placed next to videos about terrorism on a major video sharing platform. They quickly pulled their ads but were understandably concerned about any long term impacts this mistake would have on their company's image.
Obviously, this poor ad placement is something brands want to avoid—then and now. But with the explosion of online communication through videos, blog posts, social media, and more, ensuring crises like the one mentioned above don't happen again is harder than one would think.
Many platforms turned to human content moderators to try and get ahead of this problem. But not only is it impossible for humans to manually sift through and vet each piece of content—there are around 500 million tweets sent on X (formerly Twitter) each day alone—many moderators have found their mental health being negatively affected by the content they examine.
Thankfully, recent major advances in Artificial Intelligence research have made significantly more accurate, automated content moderation a reality today.
This article will look at what AI-powered content moderation is, the different approaches platforms use, how the technology works, implementation best practices, and the top APIs available for performing content moderation at scale.
What is content moderation?
Content moderation is the practice of monitoring and reviewing user-generated content to ensure it complies with platform guidelines and community standards. AI-powered content safety models automatically detect sensitive content like hate speech, violence, drugs, and alcohol across text, audio, and video using speech-to-text APIs.
Typically, the sensitive content content safety models can detect includes topics related to drugs, alcohol, violence, sensitive social issues, and hate speech.
Here's an example of what might be included as "sensitive content" by a content moderation model:
Once detected, platforms can use this information to automate decision making regarding ad placements, content acceptance, and more. The definition of what is acceptable or not acceptable may vary across platforms and industries, as each comes with its own set of rules, users, and needs.
Try AI Content Safety in Action
Test AssemblyAI's content moderation model in real-time. See how our AI detects harmful content, profanity, and more - no coding required.
Before diving into how AI models moderate content, it's helpful to understand the main strategies platforms use to review user-generated content. These approaches are not mutually exclusive and are often combined to create a comprehensive moderation system.
Manual moderation
This is the most traditional approach, where human moderators review content—like posts, comments, or videos—to determine if it violates platform guidelines.
Advantages:
Excellent at understanding context, sarcasm, and cultural nuance
Can make complex judgment calls requiring human intuition
Limitations:
Not scalable for platforms with high content volumes
Can take a significant toll on moderator mental health
Automated moderation
Automated moderation uses technology, primarily AI, to flag or remove content that violates policies. This is essential for operating at scale, as AI models can analyze millions of pieces of content in near real-time. The primary challenge is that models can sometimes lack the contextual understanding of a human, leading to potential errors.
Hybrid moderation
Most large-scale platforms today use a hybrid approach. AI models perform the initial, broad-level filtering, flagging potentially problematic content. This content is then escalated to human moderators for a final decision. This system combines the scale and speed of AI with the nuanced judgment of humans, creating a more effective and efficient workflow.
Timing strategies
These moderation approaches can be applied at different times:
Pre-moderation: Content is reviewed before it goes live. This is the safest option but can slow down user interaction.
Post-moderation: Content goes live immediately and is reviewed afterward. This promotes free-flowing conversation but risks harmful content being seen.
Reactive moderation: Content is reviewed only after it's been reported by users.
How does content moderation work?
Content moderation AI models use three primary approaches:
Generative models
Generate lists of detected topics from input text. Example: labeling "He had a cigarette after dinner" as containing tobacco references.
Classifier models
Output probabilities for predetermined categories like hate speech, violence, or profanity.
Text analysis models
Use "blacklist" dictionaries to match words against predefined categories like crime or drugs.
Model Type
How It Works
Best For
Limitations
Generative
Generates list of detected topics
Discovering new categories
May produce unexpected outputs
Classifier
Outputs probability for preset categories
Known content categories
Limited to predefined classes
Text Analysis
Matches against keyword lists
Simple, explicit filtering
Misses context and nuance
Implementation best practices and challenges
Implementing a content moderation system, especially one powered by AI, comes with its own set of challenges and best practices. Getting it right is crucial for protecting your community and brand.
Define clear and consistent policies
Define clear and consistent policies before implementing any content moderation technology.
Key requirements:
Comprehensive guidelines: Cover all types of content violations
Consistent enforcement: Apply rules uniformly across platforms
Training foundation: Serve as source of truth for AI models and human moderators
Embrace a human-in-the-loop system
AI is powerful, but it's not infallible. The most effective moderation systems use AI to handle the high volume and flag content for human review. This keeps human moderators focused on the most complex and nuanced cases, where their judgment is most valuable.
Account for context and nuance
One of the biggest challenges in content moderation is context. A word that is harmless in one context can be deeply offensive in another. AI models can struggle with sarcasm, irony, and evolving slang. It's important to continuously train and fine-tune your models with new data to help them adapt.
Plan for scale
A system that works for a thousand users may break down with a million. When choosing a solution, consider its ability to scale efficiently. An API-based approach, for example, allows you to offload the infrastructure burden and scale your moderation capabilities as your user base grows.
Scale Content Moderation with Experts
Planning for millions of posts or streams? Talk to our team about API-based workflows, reliability, and volume needs tailored to your platform.
Teams implementing content moderation face several recurring challenges:
False positives: Over-aggressive filtering can frustrate legitimate users and reduce engagement by 15-25%
Language and cultural differences: Acceptable content varies significantly across regions and communities
Evolving tactics: Bad actors continuously develop new ways to evade detection
Performance trade-offs: Balancing speed, accuracy, and cost at scale
Content moderation use cases
Content moderation has significant value across a wide range of brand suitability and brand safety use cases.
For example, smart media monitoring platforms use content moderation to help brands see if their name is mentioned next to any sensitive content, so they can take appropriate action, if needed.
Brands looking to advertise on YouTube can use content moderation to ensure that their ads aren't placed next to videos containing sensitive content.
Content Moderation APIs also help:
Protect advertisers
Protect brand reputation
Increase brand loyalty
Increase brand engagement
Protect communities
Industry-specific applications
Different industries apply content moderation in unique ways:
Industry
Primary Use Case
Key Benefit
Social Media
User-generated content filtering
Community safety at scale
Gaming
Real-time chat moderation
Positive player experience
E-commerce
Product review screening
Trust and authenticity
Education
Student forum monitoring
Safe learning environment
Media & Entertainment
Comment and reaction filtering
Brand-safe engagement
Top APIs for content safety
Now that we've examined what content moderation is and how the models work, let's dig into the top content safety APIs available today.
Ultimately, choosing a content safety API depends on your use case—some APIs interact purely with text inputs, like social media feeds, while others are adept at handling audio and video inputs, like YouTube. Other models can identify potentially harmful content in images as well.
The sensitivity of the model, as well as the accuracy, will also be important determining factors depending on your use case. An open forum may need more strict content moderation than a private one, for example.
API Provider
Type
Features
Pricing
AssemblyAI
Audio, Video
Content Safety, severity scores, confidence scores, high accuracy
$0.15 per hour for the Universal model, with bulk discounts and $50 free credits
Azure AI Content Safety
Text, Image, Video
Custom filters, generative AI detection, Azure ecosystem
$.75 per 1,000 images, $.38 per 1,000 text records, with limited free tier available
Amazon Rekognition
Text, Image, Video
AWS ecosystem, face detection and analysis, custom labels
The content moderation model detects a wide range of sensitive topics in audio and video files. It's enabled by setting the content_safety parameter to true in your transcription request. The model returns a list of detected sensitive topics, each with a confidence score, severity score, and timestamps.
Pricing for the Universal model starts at $0.15 per hour. Developers looking to prototype with Speech AI can also get started with $50 in free credits. Volume discounts are also available for teams building at scale.
Get Started with AssemblyAI's content moderation API
Pinpoint exactly what sensitive content was spoken and where it occurs in an audio or video file.
AI Content Safety is part of Azure's Cognitive Services suite of products. Its API can detect sensitive or offensive content in text, images, and video. Users can also use its Human Review tool to aid confidence in a real-world context.
Pricing for the Azure AI Content Safety starts at $.75 per 1,000 images, $.38 per 1,000 text records, with limited free tier available. Human moderation is included in its standard API pricing. Those looking to try the API should review the Start Guide here.
3. Amazon Rekognition
Amazon Rekognition offers Content Moderation for image, text, and video analysis, in addition to other Audio Intelligence features such as Sentiment Analysis, Text Detection, and more. The Content Moderation API identifies and labels sensitive and offensive content in videos and texts along with an accompanying confidence score.
You will need an AWS account, an AWS account ID, and IAM user profile to use Amazon Rekognition. Pricing varies based on usage. This guide can get you started.
4. Hive Moderation
The Hive Moderation API performs Content Moderation on all media types, including images, videos, GIFs, and live streams. The API detects more than 25 subclasses across 5 distinct classes of offensive or sensitive content, including NSFW, violence, drugs, hate, and attributes, along with a confidence score. Hive's documentation can be found here, but developers looking to test the API will have to sign up for a demo here.
5. Sightengine
Sightengine's Content Moderation API lets users moderate and filter images, videos, and texts in real time. Users can pick and choose which models they wish to apply and create their own custom moderation rules.
Pricing ranges from $29 to $399 per month depending on usage and audio/video streams needed, with a free tier and enterprise custom pricing also available.
6. OpenAI Content Moderation API
OpenAI's recently updated Content Moderation API lets developers identify harmful content in text and images and then take appropriate corrective action if needed. The API classifies content across several categories, including hate, harassment, self-harm, sexual, and violence, with more granular sub-categories for each. While free to use, the API is aimed toward developer-use and does not provide a user-friendly dashboard interface like some of the other APIs discussed.
Content moderation tutorial
Want to learn how to do content moderation on audio files in Python? Check out this YouTube Tutorial:
Ready to Add AI Content Moderation to Your App?
Join thousands of developers using AssemblyAI to create safer online spaces. Sign up now and get $50 in free credits
Frequently asked questions about content moderation
What is the difference between content moderation and content filtering?
Content filtering blocks content based on predefined criteria like banned word lists, while content moderation applies community guidelines requiring contextual understanding of intent and meaning.
How accurate is AI content moderation?
AI content moderation achieves high accuracy for clear violations like graphic violence, but struggles with context-dependent issues like harassment and misinformation. Most platforms use hybrid approaches combining AI with human reviewers for optimal results.
What are the main challenges of implementing AI content moderation?
Key challenges include handling language nuance, keeping up with evolving harmful content tactics, avoiding AI bias, and balancing automation costs with human review needs.
Is content moderation the same as censorship?
While they can seem similar, they are fundamentally different. Censorship typically refers to the suppression of speech or information by a government or other authority. Content moderation, on the other hand, is the process by which a private platform enforces its own terms of service to create a specific type of community environment. Users agree to these terms when they sign up to use the service.
How do I choose between different content moderation approaches?
The right approach depends on your platform's scale, user base, and risk tolerance. Small communities might succeed with manual moderation, while platforms processing millions of posts need automated solutions. Most successful platforms use a hybrid approach—AI for initial filtering and humans for complex decisions. Consider your content volume, available resources, and the potential impact of moderation errors when making your choice.
Title goes here
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
