The Content Moderation Glossary

When you open an online platform, whether it’s social media or an ecommerce website, it doesn’t take long to notice that things can get messy. That’s the raison d’être of content moderation — the behind-the-scenes mechanism for keeping platforms safe and user-friendly. In this content moderation glossary, we’ve compiled all important definitions you need to know.

In a nutshell, content moderation is a complex and elaborate process of monitoring and filtering content on digital platforms. Its goals are to protect users, uphold community guidelines, and respect legal requirements. 

With the exponential growth of user-generated content (UGC), the necessity for content moderation has become paramount — and digital platforms are experiencing this first-hand. Due to the sheer amounts of content being published constantly, automated content moderation is now the most effective and scalable mode that is being used across industries. 

But what exactly is content moderation and what terms do you need to know to understand both the big picture and the nitty-gritty details? 

We know the lingo can get confusing, so here is our handy content moderation glossary. It contains the key terms that will help you grasp the particularities of the moderation process and to apply it effectively in your digital business. 

The Basics About Content Moderation

The moderation process involves the screening and assessment of the suitability and safety of online content. 

Before automated AI moderation, it was up to human moderators to sift through the massive amounts of content. This required rigorous work of flagging posts and visuals and making decisions on the go. But with time, the amount of posts became unmanageably large, and the content — more and more complicated. 

Today we’re counting on technology to do the heavy lifting, while manual moderation is only necessary for setting moderation thresholds, clarifying cultural nuances, and settling delicate cases. The rise and fast development of machine learning algorithms and Natural Language Processing (NLP) allows automated moderation to make big jumps fueled by AI. 

Types of Content for Moderation

Content moderation is still complex, though, despite the huge effect of using AI in the moderation mix. 

There are different types of content to monitor, and each type requires different technology and approaches:

• Text moderation is the most common form. It entails the screening of posts, comments, and chats to prevent the spread of harassment, hate speech, offensive language, and spam. While it’s the most developed moderation type, nuances in language and expression still pose a challenge, so human review may be necessary in some cases.
• Image moderation is, to a large extent, automated, thanks to the developments in image recognition technology. It can screen for harmful, violent, and Not Safe for Work visuals. Since images can have a much stronger effect on users, image moderation is especially important for ensuring safe online environments. 
• Audio moderation often entails speech-to-text transcription, so that the moderation models can analyze the text for offensive and harmful language. It’s being used for live chats, podcasts, and other audio formats online. 
• Text-in-image moderation targets harmful and prohibited messages within images, such as memes, screenshots, and captured photos. It is deployed through a combination of optical character recognition (OCR) and text moderation tools.  
• Video moderation requires the most complex mix of technology and approaches since it contains a large number of images, as well as audio and text. We’ll look into it in detail in the next section. 

Varieties of Moderation Approaches

Besides the different types of content that need moderation, there are a number of approaches to the monitoring process. They include:

• Pre-moderation is a proactive approach in which all content is reviewed before publishing. While it ensures full protection for online communities, it is quite slow for the current digital landscape.  
• Post-moderation is a reactive approach. The content gets published, and afterwards harmful or disturbing content gets reported or flagged and then removed. It enables real-time interactions, which is great, but brings the risk of exposure to harmful content. 
• Reactive moderation is, obviously, a reactive approach in which unsuitable content is reported by users. It relies on the responsibility of community members, but may lead to the easier distribution of harmful content. 
• Manual moderation entails the human review of content. Today this is mostly used for content that has been flagged by AI monitoring. Human moderators decide on complicated and sensitive cases where nuances and context are needed. 
• Automated moderation is powered by machine learning models that can review and flag content in large amounts and at great speed. It is fast and effective, but still needs some support from human moderators in handling cultural nuances and context.  

Video Moderation: Technology on the Rise 

Moderating text is certainly not an easy feat — especially when you consider the large spectrum of nuances, cultural and social context, language diversity, and slang. 

But video moderation is yet another beast. Each frame within a video, as well as the complementary audio and potentially even text, all have to be screened and analyzed for harmful content. The task becomes unimaginably difficult when you think about the massive amounts of short-form video that needs to be screened on social media. 

The volume and complexity of video moderation have practically made it an impossible job for human moderators. AI-powered moderation platforms, however, can have a central role in this process. They can provide a high level of accuracy, immense scalability, and speed. While human input may still be needed at times to clarify borderline or complex cases, the hard work can go through the automated platform.  

With the power of image recognition and machine-learning algorithms, moderation tools can detect problematic content like nudity and violence in real time. They can be instrumental in spotting deepfakes, as well as harmful content in text within the video. Live video streams can also be monitored in this way, providing a method for  user safety even in this challenging setting. 

In our hectic digital environment, smart video moderation can be a crucial factor that promotes brand reputation and platform trust. This makes it a win-win method for both businesses and users. 

The Content Moderation Glossary

After getting to know the basics about content moderation, you’re ready to dive into the glossary. 

Here is the compilation of terms that we believe are the most relevant ones to the process and specificities of content moderation today. 

Adult Video Content Detection 

The moderation process of identifying explicit or Not Safe for Work (NSFW) video content is referred to as adult video content detection. It typically involves distinguishing between explicit, suggestive, and safe content with certain pre-set thresholds.  

API 

The abbreviation API stands for Application Programming Interface. It enables communication between different software applications. APIs are used for web apps, mobile apps, and software libraries. 

Automated Moderation

Automated moderation is possible due to the developments in AI algorithms. Moderation systems can monitor all types of content, at scale and with great speed. 

AI Regulation 

National and international bodies, as well as different organizations, create rules and guidelines for using Artificial Intelligence in various aspects of our life and work. They aim to ensure the safety and transparency of AI systems.

AI-Powered Moderation

Machine learning algorithms are being used to boost the moderation process by quickly identifying harmful and illegal content in text, images, videos, and even live streaming.  

Brand Reputation

The reputation of a brand refers to the public’s perception of that company or platform. Content moderation helps protect a brand’s reputation by ensuring a safe digital environment for its users that also adheres to legal requirements. 

Community Guidelines

This term refers to a set of rules that a digital platform creates to ensure safety and appropriateness. The guidelines define the activities that users can engage in or should refrain from. 

Computer Vision 

Computer vision is a broader technology term that includes image recognition. This technology allows computers to ‘perceive’ visual data and understand its content and meaning.  

Content Policy 

Digital platforms create content policies in order to guide their content moderation efforts. The policy contains the principles and thresholds for content monitoring. 

Copyright Issues 

In the context of content moderation, copyright issues refer to cases when users upload content whose copyright has not been cleared with the owners or authors. This may include texts, images, videos, and the like. 

Deepfakes

Images, videos, or audios that imitate real people but are used in a deceitful or manipulative way are referred to as deepfakes. Their uncontrolled distribution is a serious concern for digital platforms. 

Explicit Content

This term may include various types of content that is sexual, violent, or inappropriate in some other way. It is closely related to Not Safe for Work (NSFW) content. 

Explicit Content Detection Models 

These AI models are trained to identify, flag and remove adult content, including nudity and sexual images. They can be applied for both images and videos. 

Flagging, Review and/or Removal 

These three terms relate to the content moderation process. When content is being screened, it may get flagged by the automated moderation system because of potential issues. Then it may be reviewed additionally by human moderations. In case the issues are substantial, the content may then be removed. 

Fraud Detection 

AI systems can be used to provide an additional level of security for digital platforms through fraud detection. This involves the monitoring of fake accounts, disturbing behaviour, scam, and the like. 

Generative AI 

AI tools that can create new content, such as text, audio, images, and video, are called generative. They have been trained through deep learning on large datasets and aim to reproduce human creative output. 

Harmful Content 

Harmful content can take different forms, including text, images, audio, video, and live stream. It can be categorized as such due to potential emotional and psychological harm. 

Human and Hybrid Moderation 

Human moderation is the process of manual content review executed by people. The hybrid mode is a mixture between automated moderation and human review that ensures that high speed is matched with great accuracy. 

Image Moderation 

The moderation of images refers to the process of screening visual content for harmful and illegal elements. Today it’s handled to a large extent through automated moderation, powered by computer vision and machine learning algorithms that can ‘see’ and make decisions on the level of safety of the content. 

Image Recognition 

Image recognition is the powerful technology that enables the moderation of visual information. It is based on AI algorithms that allow computers to perceive the objects in images, such as objects, people, and scenes, and assess their details. 

Misinformation and Disinformation 

When false information is shared by mistake, this is referred to as misinformation. In the case of purposeful spread of fake information, the term used is disinformation. It’s the intention of the user that sets the two terms apart. 

Moderation Filters 

Moderation filters contain custom-set rules or settings that guide the moderation process. The filters identify specific text or visuals, so that the problematic content does not get published or gets removed quickly. 

Multi-Modal AI Models 

With the rise of video online, AI models have to handle the moderation of various types of content at once. Multi-modal models can process text, audio, video, and images to provide robust and precise content monitoring. 

Natural Language Processing (NLP)

NLP is a field of AI technology that enables computers to understand, interpret, and create human languages. This allows effective text moderation. 

NSFW

The abbreviation refers to the term Not Safe For Work, sometimes also written Not Suitable For Work. It encompasses content that is explicit, violent, or otherwise inappropriate for viewing. 

Live Streaming Moderation 

Moderation of live streaming is a complex process that involves the monitoring of different content types simultaneously and in real time — including audio, video, images, and text. It is applied for screening of live streams, video games, live chats, and the like. 

Pre-moderation

This is a proactive mode of moderation. Content is reviewed before it is published online. It ensures a greater level of protection, but is slower. 

Post-moderation 

Post-moderation is a reactive moderation mode. After being published, content may get flagged for review and removal. It allows real-time communication, but has a higher risk exposure. 

Reactive Moderation 

In this mode of moderation, content is removed only after a specific flagging from users. It is appropriate only for some types of online platforms since harmful content can be spread more easily. 

Real-Time Moderation 

The speed and massive amounts of content in today’s digital world often require real-time moderation. It refers to the process of instant detection and removal of harmful or illegal content. This is especially important for video and live streams. 

Synthetic Content / Data 

Computer generated data, usually for algorithm training purposes, is called synthetic. It can include text, images, audio, and video. Synthetic data can provide algorithms with a data alternative that respects privacy and overcomes data scarcity, but can raise bias and diversity issues. 

Text Moderation 

The process of monitoring and removing unsafe text content is called content moderation. It has been powered by the rise of Natural Language Processing (NLP). 

Text-in-Images Moderation 

This term refers to the moderation of harmful or misleading text that is embedded within visuals. This can include memes, screenshots, and other types of captured visual material.  

Trust and Safety Programs

Trust and Safety programs aim to create a wholesome framework for user protection. Digital platforms formulate them in order to foster safe online environments, guarantee their users’ privacy and security, and uphold their brand reputation. 

User-Generated Content 

UGC, or user-generated content, entails all types of content created and/or posted directly by users on digital platforms. It can span text, images, audio, video, and live streamings. 

Video Moderation

Identifying and removing video that contains harmful or explicit content is called video moderation. It’s an elaborate process that includes the review of images, audio, and even text. This type of moderation is one of the most complicated and resource-heavy. 

Explore the Power of Automated Content Moderation for Your Digital Platform  

Content moderation has become indispensable in the digital world of today — and it has become more effective and manageable with the power of AI. Applying cutting-edge moderation methods in the smartest ways is what can set your digital platform apart from the rest.

Get in touch to explore how Imagga’s content moderation solutions can be easily embedded in your workflow, ensuring maximum protection and efficacy for your digital platform.