5 Types of Content Moderation and How to Scale Using AI

5 Types of Content Moderation and How to Scale Using AI

Sorcha Sheridan


Content Marketer

November 16, 2022

User-generated content has not only dominated the digital world; with the number of reviews and opinions posted online, it has also become integral to business growth and strategy. For this reason, content moderation is now crucial within any large online community, ensuring a safe space for users and brands alike.

But what is content moderation, exactly?

In short, it is a process that regulates and monitors user-generated content by establishing pre-arranged guidelines and rules. These rules are then implemented, often through AI content moderation.

Before automating any given area of your business, it’s important to have a strategy in place. In this blog post, we discuss the different moderation methods you can consider leveraging in your business, and how to use the power of AI content moderation to scale.

How to define your content moderation strategy?

Before you work on your brand’s approach to managing user-generated content, it’s important to understand the various stages of how content is created, analyzed, and acted upon. These are:


Pre-moderation involves assigning moderators to check your audience’s content submissions before they are made public. If you’ve ever tried to post a comment and it was restricted from being published, that was a case of pre-moderation.

This method can apply to comments on products and services, and all types of media posts. The purpose is to ensure that the content is compliant with certain criteria, with the aim of protecting the online community from harm or legal threats that can negatively impact both customers and the business.

For businesses concerned about their online reputation and branding, pre-moderation is often the preferred method. It is important to be aware that pre-moderation can delay critical discussions among your online community members because the approval and filtering process removes the option of real-time interaction.


With post-moderation, real-time content submissions are allowed, and users can report content deemed as harmful after the fact.

After the reports are made, either a human or content moderation AI solution will review the flagged content and delete it if necessary. The AI review process follows the same workflow as pre-moderation, where harmful content is automatically deleted based on established criteria.

Given that content forms are so diverse today, with images, text, and audio all being used online, there are a variety of AI content moderation technologies that can be leveraged according to your business’s needs. We will discuss them further in this piece.

Example of Post-Moderation: User Reporting an Instagram Comment
Post-Moderation: User Reporting an Instagram Comment

Reactive moderation

Some online communities have established so-called ‘house rules’. These communities rely on members to flag any content they identify to be in breach of regulations, or that is otherwise offensive or undesirable.

This technique is known as reactive moderation. It can be utilized with pre and post-moderation methods, as an extra layer of protection in case the AI technology misses anything. Most commonly, reactive moderation is used as a standalone moderation method, particularly in tight-knit online communities.

In this process, members are responsible for reporting inappropriate content they come across on the platform or website. The reporting function often includes the use of a button. When clicked, it generates an alert that lets the administration check whether the flagged content was truly in breach of the site’s rules. If so, they will manually remove the content.

Distributed moderation

This method allows community members to use a rating system to cast votes on content submissions. After the ratings are submitted, the average rating score determines whether the content is successfully submitted, based on whether it is deemed in line with the community’s rules. Most commonly, the voting process is done alongside senior moderator supervision.

However, this democracy-like moderation method has some downfalls. There are both legal and branding risks involved with entrusting users to moderate content. In addition, distributed content doesn’t guarantee real-time posting or full security.

Although, on the bright side, it can encourage higher participation and productivity within the community. That being said, distributed moderation can be effective for small businesses because it can boost current resources in line.

User-generated content – a blessing or a curse?

This is not a black and white situation, and there are many ways to look at the question. On the one hand, user-generated content benefits communities by allowing members to voice their opinions, concerns, share their knowledge, etc. On the other, however, it can be overwhelming and resource-demanding to manually moderate the content.

Here are a few statistics that visualize just how challenging it is to sift through content manually. According to Statista, every minute, 240,000 images are shared on Facebook, 65,000 images are posted on Instagram, and 575,000 tweets are posted on Twitter.

Content that needs moderating includes:

Image content

Given the enormous volume of images posted online, it’s unrealistic to expect staff to conduct manual image content moderation for each and every image. Not only are there not enough hours in the day, but the assessment process is subjective and inconsistent among different moderators.

Although, in the beginning, it can be effective to have users report content violations, this isn’t a reliable strategy in the long run. What might be offensive in one moderator’s eye can be perceived as neutral by another. In addition, hours of manual content moderation runs the risk of causing eye strain and fatigue, negatively impacting the health of employees.

Text data

Like images, the volume of text data posted online is simply too much to evaluate manually. We also see the same issue of subjectivity in the human evaluation process, and the potential for brand content inconsistency.

Without content moderation AI, text data would be impossible to review. We will discuss this next.

AI-powered moderation

As made evident by the statistics, there is a misalignment between the amount of UGC posted online, and human moderation capabilities. This leads us to a solution for companies wanting to effectively moderate their content: content moderation automation.

Levity flow for moderating user-generated content through AI
Levity flow for moderating user-generated content

Content moderation using AI can support human moderators in their review process and allow companies to scale faster given their resources. The ability to accurately identify and quickly remove inappropriate content is essential to the safety and comfort of community members, as well as the overall reputation of your site.

There are many customized AI content moderation methods that can be used for moderation, with the exact solution depending on the content type.

For text

Natural Language Processing algorithms are used to understand the intended meaning behind text and decipher the emotions. Text classification allows categories to be assigned to the text or sentiment based on the content.

As an example, Sentiment Analysis can identify the tone of a given text and group it into the categories of bullying, anger, harassment, sarcasm, etc., and go on to label it as either positive, neutral, or negative.

Entity recognition is another AI content moderation technique that extracts names, locations, and companies. This form of content moderation AI strategy can let you know the number of times your brand was mentioned on a particular website, or even how many people from a particular location are posting reviews of your brand.

The exact technology includes:

  • Natural Language Processing – Here, we can have actions such as keyword filtering (linking keywords to certain sentiments and forming categories like positive, neutral, and negative), and sending alerts to the moderation team based on topic analysis (for sensitive keywords that can imply crisis, brutality, and age-sensitive content).
  • Accessing previously reviewed content and knowledge bases – By looking at previous databases, computers can reliably predict content that is fake news or a common scam.

For voice

When it comes to voice, we’re looking at a technology known widely as voice analysis. It leverages several other AI-powered solutions and can include things like translating voice to text, running NLP and Sentiment Analysis, to even interpreting the tone of voice.

For images

Image content moderation automation uses text classification alongside vision-based search techniques. The techniques involve the use of different algorithms that detect harmful images and then locate the particular harmful content’s position on the image.

Content moderation AI for images also leverages image processing algorithms to identify areas inside the image and create categories based on a chosen criteria. If there happens to be text within the image, object character recognition (OCR) is able to moderate the entire content piece.

These image content moderation AI techniques allow for the detection of offensive or abusive words, and any objects or body parts within unstructured data. After the content is approved, it can be published, whereas flagged content gets sent to the next stage of manual moderation.

Computer Vision

Computer Vision is a subcategory of AI, which trains computers to comprehend and analyze the visual world in order to identify harmful images. The AI content moderation comprehends, tags, and if needed, notifies the moderation team of any offensive and disturbing content.

Example of a system using computer vision
Example of a system using Computer Vision. Source: Huawei

For video

Video content moderation automation uses a mix of the previously discussed voice analysis, text, and image technology.

Let your employees do more – and in a better/safer manner

By implementing content moderation AI, you can relieve your moderators of the huge burden of manually reviewing all content submissions. This translates to increasing their productivity and minimizing the risk of potentially harmful effects of content moderation.

Customized AI content moderation can improve the entire process, by using the above techniques to prioritize content that needs to be further reviewed by a human. Prioritization is based on the level of perceived harmfulness to the community or uncertainty from the AI moderator.

AI content moderation can also lessen the impact on human moderators by being cautious about what types of harmful content they see. The AI can blur certain images to limit exposure to the most offensive and harmful elements, which the moderator can then choose to view if they need to come to a moderation decision.

Visual question answering is an AI technique that allows humans to ask a series of questions to gauge the level of harmfulness that a piece of content may have, but without viewing it directly. This method is less reliable than when human moderators directly view the content, but on the positive side, it can reduce the harmful effects of seeing certain types of content.

Content moderation using AI is also possible in different languages, thanks to accurate translations.

Distribute your resources where most needed (i.e., Human in the Loop)

At the end of the day, technology is meant to help us save time and make intentional choices on how and when we decide to use our human capabilities. As a rule of thumb, we want to make the most of content moderation AI and human decision-making processes.

Machine Learning models are man-made and rely on the data we feed them, meaning they’ll only be as good and as accurate as the input we choose to power them with. There is a risk that comes with letting AI have full control, namely making wrong predictions, or AI bias. That being said, the concept of ‘Human in the Loop’ (HITL) can be put in place to prevent this.

HITL is essentially referring to systems where humans can give direct feedback to a given model so that it can make predictions (below a chosen level of confidence).

When choosing the level of confidence, you need to take into account the potential consequences of allowing wrong predictions to pass through the system. Setting a lower threshold will mean that less human intervention will be needed. Whereas in other cases, the room for error is much lower and you want the system to only display correct predictions.

Scale at speed

Content moderation automation allows you to review content faster than what’s possible by manual processes. A major benefit of the pre-moderation approach is that it can process huge amounts of data posted online at every given second while allowing for almost real-time interaction among community members.

As discussed above, content is submitted to social media in the hundreds of thousands each minute, and it would be very uneconomic and inefficient to employ manual review systems to address this.

Data moderation should happen at a pace that comes as close to real-time as possible in order to protect users from harmful content while still enabling them to have meaningful interactions.


With the increase of user-generated content (UGC) being implemented across a wide range of industries and not just social media, it’s clear that this phenomenon is not going anywhere.

In today’s world, UGC encompasses images, text, video, and audio. New forms are expected to arise in the future. Any business using UGC as part of its strategy needs to have a system in place to handle the moderation process. It’s the only way to make sure that the reputation and user experience are pleasant and in line with branding.

Looking forward as you scale your business, it’s important to be mindful of how you allocate your resources and human efforts. AI-powered automation under human supervision is the most efficient way to moderate content and continue scaling. To achieve this, building out a content moderation AI strategy is key.

Now that you're here

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you'll love Levity.

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Now that you're here

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you'll love Levity.

Sign up

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