What Is AI in Content Moderation?
Content moderation is the practice of reviewing user-generated content (UGC) on internet platforms – social media, forums, and other online outlets – to assess its appropriateness. The content is checked for compliance with the platform's standards, regulations, or jurisdictional laws. This process is crucial for maintaining a safe and respectful online environment. Check out our complete guide on content moderation here.
AI content moderation refers to the use of artificial intelligence to automatically monitor, review, and filter online content. While it speeds up the moderation process, AI still requires human oversight to handle complex or nuanced situations.
Types of AI Moderation and Where They Are Employed
Pre-moderation
Pre-moderation involves the review and filtering of content prior to its publication on a platform. AI algorithms are employed to examine and identify unsuitable content based on a set of guidelines and standards. This form of moderation is commonly used in environments that require strict control of content quality and compliance – such as news websites, educational platforms, children’s websites.
Post-moderation
In contrast, post-moderation entails the assessment and regulation of content after it has already been posted. AI algorithms continuously monitor user-generated content in real-time, promptly flagging and removing any breaches of community guidelines. Post-moderation is often preferred on platforms with high user interaction rates and significant amounts of content, like comment sections on social media, Telegram channels, and live-streaming services.
Reactive Moderation
Reactive moderation depends on user reports to identify problematic content. AI-powered systems aid moderators by prioritizing and managing reported content. This type of moderation is employed on platforms where users actively report content – for example, on marketplaces, social media, or in gaming communities.
Proactive Moderation
Proactive moderation uses AI algorithms to automatically detect and remove inappropriate content before its publication on a platform. By examining user behavior patterns, employing sentiment analysis, and contextual clues, AI can detect harmful content early on. Proactive moderation is crucial in safeguarding against cyberbullying, hate speech, and misinformation. It serves as a valuable element in E-commerce and E-learning.
Hybrid Moderation
Hybrid moderation combines different approaches mentioned above to establish a comprehensive and adaptable system. By leveraging various moderation methods in tandem, platforms can achieve a balanced approach tailored to their specific needs and target audience. The hybrid approach offers increased flexibility and scalability in the dynamic online world.
How Do AI Moderation Systems Work?
The Training Process
Content moderation powered by AI uses algorithms to review and evaluate user-generated content across platforms. First, the AI system gathers large amounts of content from various sources – social media, forums, online stores, news websites, game streaming services, etc. This content is then used to train the ML model with the help of labeled datasets – content that is organized into categories based on specific criteria.
Throughout the training process the AI model learns to identify patterns and characteristics associated with different types of content, such as spam, hate speech, nudity, violence, or misinformation. It extracts features from the content that offer insights into its nature and context.
AI Moderation Step by Step
Although the specific approach to AI moderation depends on the type of content, it usually follows these steps:
- Content Upload
Content moderation usually kicks off when users submit text, images, audio, or videos to a platform or website.
- AI Analysis
The uploaded content is then analyzed by AI algorithms using various techniques – e.g., Natural Language Processing or computer vision.
- Flagging Content for Review
If the AI determines that content is potentially harmful or inappropriate, it gets flagged for human moderators to review.
- Human Moderation
Human moderators then carefully assess flagged content to check if it breaches community guidelines or legal requirements. They consider the context and nuances before deciding whether to approve, reject, or send the content for further review.
- AI Optimization
During this phase AI algorithms analyze feedback from moderators to enhance their accuracy in identifying harmful content. They also employ reinforcement learning methods to refine performance based on previous experiences.
AI-powered Methods for Content Moderation
AI tools and methods applied in moderation vary depending on the type of content at hand.
Text-based Moderation
For text, AI tools often use natural language processing (NLP) techniques to understand and moderate content. These include:
Sentiment Analysis
Determines the sentiment of a text, identifying potentially harmful content like hate speech, threats, or bullying.
Keyword Filtering
This method blocks or flags content based on predefined offensive or harmful words. It’s simple yet effective.
Contextual Understanding
More advanced AI models understand the context and nuances of language and can distinguish between harmful and harmless usage of words.
Image-based Moderation
AI in image moderation leverages computer vision techniques, including:
Image Recognition
Identifies harmful elements within an image: weapons, nudity, or symbols associated with hate speech or violence.
Optical Character Recognition (OCR)
Extracts and moderates text within images. OCR can be used to identify offensive language in memes or screenshots.
Object Detection
Locates multiple objects within an image, assessing each for potentially harmful content.
Deep Learning Models
Deep Learning models can understand the context and nuances of images and differentiate between acceptable and unacceptable content based on learned patterns.
Audio-based Moderation
Audio content moderation uses various AI techniques such as:
Sound Analysis
Identifies non-speech audio elements that might be harmful, like gunshots or explicit sounds.
Voice Recognition
Detects voice patterns and can flag content based on known voiceprints of banned individuals.
Speech-to-text Transcription
Converts spoken words into text, which can then be moderated using text-based AI tools.
Video-based Moderation
Video moderation combines techniques from text, image, and audio moderation:
Frame-by-frame Analysis
Uses image recognition to analyze each video frame for harmful visual content.
Audio Track Analysis
Separates and examines the audio component of videos using speech-to-text and sound analysis.
Contextual Understanding
Advanced AI models assess the video in its entirety. They consider the interplay of visual elements, audio, text, and context to determine video’s appropriateness.
Pros and Cons of AI Content Moderation
While AI moderation is becoming more and more advanced, it's important to take into account both its benefits and limitations.
| Aspect | Aspect | Cons |
|---|---|---|
| Efficiency | High-speed processing, can handle large volumes of data | May miss nuances, leading to errors |
| Consistency | Applies rules consistently and uniformly across all content | Struggles with context, may enforce rules that don’t apply to a specific content |
| Scalability | Easily adjusts to increased data amounts | Requires continuous training and updates |
| Cost-effectiveness | Reduces the need for large human moderation teams | High initial setup and ongoing maintenance training costs |
| Objectivity | Unbiased by human emotions | May lack understanding of cultural and contextual subtleties |
| Real-time Moderation | Instant action against policy violations | Prone to making snap judgments without deeper analysis |
Best Practices in AI Moderation
Incorporating artificial intelligence into content moderation requires careful planning and execution to ensure it’s effective and fair.
Combine AI with Human Supervision
This can help you capitalize on the strengths of both automated and manual review processes. While AI’s benefits include handling big volumes of content efficiently, human moderators play a vital role in understanding context and nuances that AI might overlook.
Having human support for complex cases ensures that AI won’t make final decisions in sensitive situations.
Ensure Transparency and Accountability
Users should be able to understand why their content was moderated and have the option to appeal decisions. Keep track of moderation decisions – it can aid in auditing and improving the system.
Continuously Train and Update the AI System
Constant learning from diverse data sets can help AI systems avoid biases, adjust to new forms of harmful content, and navigate in the ever-evolving landscape of online content.
Prioritize User Privacy and Data Security
Keeping the data used in the moderation process safe and ensuring compliance with privacy regulations are crucial for upholding user trust and legal requirements.
Customize the AI Moderation System
AI should be tailored to the needs of your platform. Customizing the system to fit the content formats, audience, and community guidelines of the platform can enhance the accuracy and efficiency of moderation.
AI Use Cases in Content Moderation
Fake News and Misinformation
Fake content takes various forms – from product reviews and deepfakes generated by AI to misleading news and articles. This content is gaining traction, with more than 500,000 deepfake videos circulated on social media in 2023 alone and bots spreading misinformation about serious issues, such as Covid-19.
When addressing the issue of fake news and misinformation, AI systems use methods such as text analysis, source verification, and pattern recognition to evaluate the reliability of information. AI content moderation models collaborate with fact-checking services to confirm content accuracy and flag misinformation, preventing the spread of fake information.
Child Safety
One crucial application of content moderation is protecting minors from online exploitation and adult content. According to Statista, more than 500 hours of video were uploaded to YouTube every minute in 2022. Evidently, human moderators can’t look through such enormous amounts of content alone – that’s where AI comes into play.
AI systems can identify images and videos containing child exploitation material with acute accuracy and take immediate action. These systems are regularly updated to adapt to the changing strategies of offenders, ensuring effective defense against illegal content.
Spam and Fraud Detection
AI plays a role in combating spam and fraud through advanced pattern recognition algorithms that spot anomalies and typical traits of harmful content. This not only includes textual spam in emails or comments but also sophisticated scamming schemes in online marketplaces or social networks. By drawing insights from historical data, AI models can prevent fraudulent activities and safeguard users from potential risks, while bolstering platform credibility.
Abusive Content
AI algorithms demonstrate proficiency in recognizing abusive content – hate speech, cyberbullying, abusive behavior, nudity. Online abuse is a massive issue: according to recent research, almost 41% of adults have come across online harassment in the U.S. alone.
AI systems are able to detect such content by using natural language processing (NLP) and ML techniques to analyze text, audio, images, and videos. These systems are trained on datasets that include examples of abusive content.
Software for AI content moderation
The AI moderation market offers countless tools to automatically detect harmful content across online platforms. Here are a few examples:
WebPurify
WebPurify is an affordable tool that offers image, text, video, and metaverse moderation. It can be integrated both into a website or an app. This software's AI system identifies images with the highest chances of containing harmful content, detects profanity and filters explicit content from videos and live streams.
WebPurify is trusted by global brands, such as PBS and Microsoft. This tool analyzes thousands of videos, half a million images, and 3,5 million text submissions daily.
Key features include:
- Real-time image, text, and video moderation
- Deep learning technology
- API that integrates with various platforms
- 15 languages support
Lasso Moderation
Lasso Moderation is a software designed to moderate conversations and images online, focusing on social media and customer engagement channels. It combines advanced AI technology with human oversight to ensure high-quality moderation and engagement. The tool uses sophisticated algorithms to understand the nuances of human conversation, including slang, idioms, and cultural references.
With Lasso Moderation, businesses can integrate AI in customer conversations. Moreover, the AI technology can identify harmful comments across social media to uphold the brand's reputation.
Key features include:
- Sentiment analysis
- Intent recognition
- Real-time text and image moderation across various online channels
- Engagement tools for user interaction
Amazon Rekognition
Amazon Rekognition is an AI service by Amazon Web Services (AWS) that provides image and video analysis. This software helps moderate content on E-commerce websites and social media by flagging inappropriate instances.
Amazon Rekognition has a powerful deep learning technology that offers highly accurate facial recognition, object and scene detection. It alleviates the workload of human moderators: they only have to review 1-5% of all the content.
Key features include:
- Facial analysis
- Image, text in image, and video moderation
- Integration with other AWS services for enhanced analysis and automation
- Two-level labeling system
Why Is Content Moderation Important?
The abundance of content created by users has emphasized the importance of moderation for internet platforms. According to Polaris Market Research, the global user-generated content platform market is expected to reach $20 billion by 2028. With billions of users engaging every day, platforms face reputational and financial risks due to harmful content. Content moderation maintains a safe user environment, upholds community guidelines, and protects platforms from legal issues.
Challenges in online content moderation
Large Data Amounts
Content moderation has become increasingly tough for brand websites and social platforms because of the amount of content created by users. With 5.17 billion social media users in 2024 and the growing use of mobile devices, content can be shared anytime and anywhere. To manage this scale, platforms use advanced machine learning algorithms to filter and assess content automatically. However, automated solutions alone are insufficient due to the potential for errors and the need for context-sensitive judgment.
Variety of Content Types
The various types of content available also present a challenge. Users post text, images, audio, videos (including live streams) – each content type requires moderators to manage unique sets of issues and employ different AI technologies.
New Ways to Spread Harmful Content
Users are discovering new methods to spread inappropriate and harmful content on online platforms. Fake bot accounts and private messaging services are a couple examples of how damaging content can circulate without an oversight.
How to Deal With These Challenges?
By employing machine learning algorithms, platforms can benefit from automated content moderation. AI can filter and review UGD, then flag harmful content that violates community guidelines or legal standards. This automation streamlines the moderation process, alleviating moderation teams’ workload and enabling them to focus on tasks that require direct human judgment.
Conclusion
AI has emerged as a powerful ally to human moderators. By using automation, platforms can expedite the content moderation processes, ensure scalability, and, most importantly, secure a safe environment for internet users.
