In Content Moderation, Visual Search, NLP

5 Types of Content Moderation and How to Scale Using AI

By Thomas Molfetto

Not all content is created equally.

User generated content (UGC) is not just found on social media sites anymore. More and more companies in different industries are using user generated content to drive revenue and build brand thought-leadership and loyalty. Automotive companies, restaurants, travel and e-commerce sites are all leveraging UGC to persuade potential customers to choose them. A recent study recorded 85% of people trust user generated content over company generated content. Which supports the importance of moderating UGC.

Why content moderation?

Not all UGC is about images and video. Many times, inappropriate language is used. Abusive language is often reported on the internet, and that’s why it’s important to screen for it. User generated image, video and text content can’t simply be posted to a website or social media page. It has to be monitored and reviewed first. Think of what could happen to your brand and your community if something illicit was posted. Not only would your reputation be damaged, your community members would also be affected, and you open yourself up to liability issues. There is some content, for example child sexual exploitation material (CSEM), that whether you know you are in possession or not, is illegal to have on your servers. Also there are mental hazards to human moderators of seeing this toxic, inappropriate content on a day to day basis. Lawsuits have been brought over moderators experiencing PTSD from their daily job routines. For these reasons, content moderation is becoming a critical function in so many businesses with an online presence.

...so what type of content moderation do I choose?

There are 5 common types of content moderation. The 6th being not moderating content at all, which is never a good idea and can send your community into a tailspin. The other types will definitely maintain a sense of order within your community.

Pre-moderation

It’s precisely that—content moderated before it’s posted on a website. Pre-moderation, by a good moderator, ensures that inappropriate content is flagged and kept from being posted. While it provides high control of what content ends up being displayed on your site, it has many downsides. It delays the content from being published, which in today’s time of instant gratification, people want and expect to see it immediately. This method can be costly as your content scales. It’s ideally for communities with a high level of legal risk, such as celebrity-based or children's’ communities, where protection is vital. For non-conversational or time-sensitive content, such as reviews or photos, it can be used without affecting the community too much.

Post-moderation

This type of content moderation is done after the content is posted. While preferred by users, it can bring up a host of problems for the company. As the community grows, resources needed grow, and costs become a factor. Keeping up with the volumes of content to moderate and unposting quickly can become an issue. From a legal standpoint, the website owner legally becomes the publisher of the content, as each piece of content is viewed and approved or rejected, which can expose them to liability risk.

Reactive Moderation

This type of content moderation puts the responsibility on the user community to flag and report inappropriate content. It can be used alongside pre-and post-content moderation techniques in case anything gets past the moderators. Most of the time, this is used as the sole form of moderation. The main advantage of this moderation type is that you can scale with your community growth without putting extra strain on your moderation resources or increasing costs. You can theoretically avoid responsibility for defamatory or illegal content uploaded by the users, as long as your process for removing content upon notification happens within an acceptable time frame.

Distributed moderation

Less used and prevalent is content moderation that is reliant on the audience. Basically a self moderated approach with a rating system thrown into it. The content is published on the website directly, and then users vote whether the submissions are appropriate with the community guidelines or rules. The users are the ones in control of the comments or posts with some guidance from human moderators.

Automated moderation

Automated content moderation is the most common type of content moderation method. This involves the use of computer vision, natural language processing and AI. Not only can images be moderated, but also textual content and even text within images can be screened. Using AI models, content can be reviewed and filtered automatically—faster and at scale. Inappropriate content can be flagged and prevented from being posted almost instantaneously. All of this can be used to support human moderator’s work in order to speed the process with greater accuracy.

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How is artificial intelligence accelerating the moderation of images, video and text?

The challenge faced by so many companies is how to detect toxic content faster and remove it before it’s seen. Content moderation powered by artificial intelligence (AI) enables online enterprises to scale faster and optimize their content moderation in a way that’s more consistent for users. It doesn’t eliminate the need for human moderators, who can still provide ground truth monitoring for accuracy and handle the more contextual, nuanced content concerns. Each type of content requires different types of techniques to moderate it.

Image moderation

Image moderation makes use of text classification and computer vision-based visual search techniques. These techniques utilize different algorithms to detect harmful image content and then locate its position in the image. Image moderation also involves the use of image processing algorithms for the identification of various regions inside the image and then categorizes them based upon specific criteria. Additionally, If there is textual content within an image, object character recognition (OCR), can identify text and moderate that as well. These techniques allow you to detect abusive or offensive words, objects and body parts within all types of unstructured data. Once the content is deemed acceptable it can be published and content which has been flagged as inappropriate is sent for manual moderation.

Video moderation

Moderation of videos use computer vision and artificial intelligence methods. Unlike moderating images, where the inappropriate content is immediately apparent on the surface, moderating videos requires you to actually watch the video in its entirety or review it frame by frame. In the case of end-to-end moderation, the video needs to be reviewed in its entirety in order to verify the appropriateness of both the audio and visual content. In the case of still image moderation, shots are captured at multiple intervals, and computer vision techniques then review those shots to make sure the content is appropriate.

Text moderation

To understand text and its intended meaning, natural language processing algorithms are used to summarize the meaning of the text or gain an understanding of the emotions within that text. By using text classification, categories can be assigned to analyze the text or sentiment based on the content. For example, sentiment analysis identifies the tone of the text and categorizes it as anger, bullying, sarcasm, etc. and then labels it as as positive, negative or neutral. Another, technique often used is named entity recognition. It automatically finds and extracts names, locations and companies. For example, you can track how many times your brand is mentioned in online content or competitor mentions or even how many people from a particular city or state are posting reviews. A more advanced technique involves knowledge base text moderation. It makes use of built-in databases to make predictions about the appropriateness of the text. The predictions may be classified as fake news or scammer alerts, etc.

AI as the logical progression for moderating content

The reality is, there is simply too much UGC for human moderators to keep up with, and companies are faced with the challenge of effectively supporting them. AI automation is helping support human moderators by speeding the review process. As the volume of user generated content grows, AI is enabling companies to scale quickly using the resources they have. Being able to find inappropriate content faster with greater accuracy and remove it before it’s seen and paramount to maintaining a reputable and safe community website.

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