The world is increasingly digital and more and more day-to-day interactions take place online. The amount of user-generated content associated with that — comments, posts, product reviews, images, videos, user profiles, and more — is constantly growing across social media sites, blogs, ecommerce sites, dating apps, and forums. 

Any interaction or piece of content can make or break a brand’s reputation — a single post can damage years of positive sentiment building — so it’s vital to keep your organization’s online spaces as safe and positive as possible. This is where content moderation comes in.

If your online space contains unsafe, abusive, or spammy content, people won’t engage or return — and that’s not good for business.

But how do organizations manage their user-generated content, and how does AI content moderation fit into the bigger picture? 

What Is Content Moderation?

Content moderation is the process of managing user-generated content and filtering content that goes against the specific guidelines for that platform or space. It can also involve suspending or banning users who violate guidelines. 

Different types of online spaces call for different guidelines — for example, while online marketplaces are designed for buying and selling items, a forum might prohibit “for sale” posts.

Types of Content Moderation

There’s more than one way to moderate content. Here are five types of content moderation and how they work:

• Manual Pre-Moderation — Users submit content, and it’s placed in a queue to be checked by a moderator. Nothing gets published without first being approved. This content approval method offers the highest level of control, but it also makes real-time interactions impossible. Most internet users expect instant gratification, so having to wait for mod approval can drive users away.

• Manual Post-Moderation — User-generated content is published right away, but is placed in a queue to be reviewed by a moderator after the fact. Post-moderation allows for real-time publishing, fostering authentic engagement within the community. It works well in environments where swift interactions and immediacy are crucial, and where the user-generated content is usually benign. The risk lies in striking a balance between immediacy and safety — and the more content submitted, the longer it could take moderators to reject potentially sensitive content. 

• Reactive Moderation — User-generated content is published immediately. Community members flag content that raises concerns, and moderators then review the flagged content. This collaborative approach relies on community-driven filtration. It fosters a shared sense of responsibility, but requires an engaged community and potentially risks exposing members to sensitive content.

• Distributed Moderation — User content generally goes live immediately, and the community actively participates in oversight through voting. For example, a comment could be automatically removed if a certain number of users flag it as inappropriate. This decentralized approach requires a highly engaged and active community, and is best incorporated with other methods. 

• Automated Moderation — User content goes live immediately, and is evaluated using any number of technologies — including word filters, ban lists, natural language processing, computer vision, and user behavior analysis — according to predefined guidelines. These automated tools are great at processing large amounts of data quickly, but lack the human insight and empathy often required to make complex decisions.

Each method has its strengths and weaknesses, so companies often combine them to maximize effectiveness. The best approach for your organization will depend on your available resources, as well as your platform’s specific guidelines, volume and variety of user-generated content, user demographics, and need for real-time interactions.

Content Moderation Challenges 

Managing large volumes of content poses a significant challenge for content moderation, especially as the scale of your platform or online community increases. 

This is why so many organizations struggle to scale up their content moderation as they grow — with so much user-generated content uploaded every day, how can moderators keep up without slowing down the exchange of information or missing harmful content?

Another key concern is the mental health toll on human moderators who review harmful content day in and day out. This issue is often compounded by large volumes of content, as some companies increase pressure on moderators to meet quotas during times of rapid growth. 

Luckily, AI can help solve both of these challenges.

AI Content Moderation

AI systems are capable of processing and analyzing vast amounts of content much faster — and often more accurately — than humans. This makes AI-based content moderation solutions far more scalable than those that rely solely on human moderators. 

But don’t discount the value of human moderators!! At SupportNinja, we believe that humans and AI are better together, and the most effective approach to content moderation is AI-enabled, leveraging AI technology to assist human moderators.

Technology commonly used for AI-enabled moderation includes:

• Natural Language Processing (NLP) — Analyzes and interprets human language. Can be used to identify potentially abusive language, even without the presence of explicitly banned words or phrases.

• Computer Vision — Analyzes and categorizes images and videos to flag harmful or unwanted content. Can be trained to identify things like nudity, drug use, weapons, and deepfakes. 

• Sentiment Analysis — Analyzes the overall tone and sentiment of content. Research shows that sentiment information is a better indicator of content toxicity than words alone. 

• User Behavior Analysis — Analyzes user behavior data to flag suspicious patterns (e.g. many accounts associated with the same IP address or an account that is spamming comments).

The best way to incorporate AI into your content moderation workflow will depend on your platform, policies, and the moderation strategy you use.

Most commonly, AI does an initial sweep, removing inappropriate or sensitive content (in cases with high confidence that it’s against community guidelines) and flagging more nuanced content (in cases with lower confidence) for human review. 

In addition to making the workflow more efficient, this first line of defense can greatly reduce human moderators’ exposure to harmful content.

Some AI can also improve your workflow by automatically identifying categories of violations and prioritizing them according to your brand guidelines (e.g. removing hate speech gets priority over removing spam comments). This ensures the most harmful content is removed first. 

With feedback from humans, AI can become more accurate and more independent over time, so it can scale with your content volume without sacrificing accuracy. 

Ready to Moderate Content at Scale?

In our vast, interconnected online world, user-generated content is everywhere — and it can be hard to moderate, especially during times of rapid growth. 

But with the right balance of human moderators and AI technology, you can safeguard your brand’s reputation by making your platform or online community safer and more enjoyable to use.

No matter the size of your community or your unique guidelines, SupportNinja can help you manage user-generated content with a combination of scalable, AI-enabled strategies and a dedicated team of content moderation experts. Let’s protect your platform together. 

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