Content Moderation Using AI
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Content Moderation Using AI – Need, Types & Roles

Musnad E Ahmed

Musnad E Ahmed

Last modified on October 30, 2023

Content Moderation Using AI – Need, Types & Roles

Online platforms that allow users to engage and publish information for others to view have become an essential part of many people’s lives and have benefited society in the last decade.

But, the general public, corporations, and lawmakers are becoming more aware of the dangers of harmful internet content. User-generated content (UGC) adds to the diversity and depth of info on the web, but it is not limited to the same editorial constraints as conventional media.

As a result, some individuals can upload material that may cause harm to others, especially youngsters or the elderly who are oblivious. Content that is harsh and disrespectful to others supports terrorism or displays child abuse are examples of this.

So, what’s the solution?

AI in Content Moderation

As the degree of UGC uploaded by online users grows, conventional human-led monitoring systems can no longer determine and delete inappropriate content at the actual rate required.

Before going on with the technical aspect let’s start with a real-life story and experience. Recently, In Bangladesh, a person came live on Facebook who was living a depressing and troubled life. Many people joined that live to hear him talk. He was sharing his tales of misery and suddenly pulled out his gun and shot himself, committing suicide in the process. Many watched this. No doubt it was a sensitive and disturbing thing to watch.

So, before it could spread any further, Facebook took the live down and is still trying to take it off from its platform under sensitive content. In such instances, AI is playing a huge role in detecting live images, videos, and short clips.

It is really important because nowadays social media platforms are accessible to all who use the internet so this kind of content going viral and creating a disturbance in our mind is not avoidable with only human content moderators. The amount of data is huge and can only be moderated through artificial intelligence.

This article will explain the capabilities of artificial intelligence (AI) technologies in meeting the challenges of moderating online content and how improvements are likely to enhance those capabilities.

Machine Learning

This article will explain the capabilities of artificial intelligence (AI) technologies in meeting the challenges of moderating online content and how improvements are likely to enhance those capabilities.

Machine learning, which allows a computer system to make judgments and anticipate results without even being supervised learning to do numerous tasks, has largely fueled recent breakthroughs in AI.


These neural network models, the invention of ‘deep neural networks,’ which enable ‘deep learning,’ is the most prominent advancement in machine learning in recent decades. This method needs either data collection or instructional strategies that the system may explore.

These neural network models detect characteristics in complicated data inputs like natural voices, pictures, and text. These systems’ proficiency in doing the specific job is almost perfect for many scenarios. They are being constantly taught currently, which is comparable to humans.

All Contents Are Not Generated In The Same Way

User-generated content (UGC) is no longer limited to social media platforms. User-generated content is being used by a growing number of businesses across various sectors to raise profits and create brand innovative solutions and affinity.

UGC is being used by automotive companies, hotels, travel agencies, and e-commerce sites to urge potential clients to pick them. According to recent research, 85 percent of respondents prefer user-generated content to material created by companies. This emphasizes the significance of controlling user-generated content (UGC).

Content Moderation and The Need

Images and video aren’t the only forms of user-generated content. Offensive speech is frequently used. Here on the web, offensive language is commonly reported, which is why it’s critical to check for it.

A webpage or social media platform cannot simply upload a user-generated picture, multimedia, or text material. It must first be analyzed and investigated. Consider the impact on your business and communities if something illegal was shared.

Not only would your image be harmed, but so would the people of your network, and you would be exposed to liability difficulties. Some material or content is available, such as child sexual exploitation material, that is prohibited from hosting on your systems if you are aware of it or not.

There are also psychological risks for human censors exposed daily to this poisonous, improper stuff. Lawsuits were filed on behalf of moderators who have developed PTSD due to their regular work patterns. As a result, content filtering has become an increasingly important job for many organizations with an internet persona.

Types Of Content Moderation

There are five different kinds of content moderation. They are :

  1. Pre-moderation
  2. Post-moderation
  3. Reactive Moderation
  4. Distributed moderation
  5. Automated moderation

Types of Content Moderation

Here in this article, we will discuss automated moderation, which is content moderation using AI( Artificial Intelligence).

The most popular sort of content control approach is automatic content moderation. Machine learning, natural language processing(NLP), and artificial intelligence are all used in this process. Even images and No graphics can be monitored, written material and even text inside pictures.

Content may be examined and regulated autonomously using AI models, which can be done more quickly and in bulk. Quite instantly, improper information may be detected and stopped from being uploaded. This may assist human moderators in their work, speeding up the process and improving precision.

Role of AI in Multimedia & Text Moderation

Many businesses are grappling with identifying harmful content faster and deleting it before it becomes viral.

Artificial intelligence (AI)-powered content moderation helps internet businesses to expand more quickly and improve their content moderation in a much more systematic way for consumers.

Human moderators are still required to offer test datasets, check for reliability, and manage the more relevant, complex content problems.

To control different sorts of information, different strategies are required.


AI in Image Moderation

Textual segmentation and computer perception image search algorithms are used in image moderation. Different algorithms are used to detect dangerous visual material and determine its location in the graphic.

Image moderation also entails the application of picture processing algorithms to identify and classify distinct sections within an image based on specific criteria. Furthermore, object character recognition (OCR) may detect and regulate the writing if an image contains the text.

These methods may be used to detect unpleasant or derogatory phrases, symbols, or nudity or sexual content, or large datasets of any form. Information that is judged suitable can be published, while content identified as improper is forwarded for human moderation.

AI in Video Moderation

Generally. In videos, AI is used for face recognition, automated computer vision, and artificial intelligence moderate videos. Unlike reviewing photos, where incorrect information is readily evident, regulating videos necessitates watching the entire video or going through it in real time.

In the case of edge moderation, the video must be examined in its entirety to ensure that both the visual and audio material is proper. In the instance of still picture moderation, photos are taken periodically and then reviewed using computer vision algorithms to ensure that the content is suitable.

AI in Text Moderation

NLP (Natural Language Processing) algorithms summarize the meaning of a document or acquire a grasp of the sentiments inside that text to comprehend it and its underlying implications. Text categorization allows classifications to assess the text or emotions depending on the content.

Sentimental analysis, for instance, recognizes the text’s mood and classifies it as rage, hostility, humor, and so on before labeling it as good, bad, or ambiguous. Isolated word detection is another popular approach. It is also widely known as entity recognition. It detects and retrieves identities, places, and businesses autonomously.

You may, for instance, keep track of the number of your company referenced in web pages, rival references, or the number of individuals from a specific area or province who leave ratings. Knowledge-based text moderation is a more refined strategy.

It provides suggestions about the suitability of the content using constructed or built-in resources. The forecasts might be labeled as misleading news, fraud alerts, and so forth.

AI Is The Natural Step For Content Moderation In the Future

The fact is that there is simply too much User Generated Content (UGC) for human moderators to maintain pace with, and businesses must figure out how to assist them efficiently. Through accelerating up a monitoring and moderating process, AI technology is helping human moderators.

Because the number of user-generated content expands, AI allows businesses to broaden swiftly while utilizing their existing infrastructure. The ability to quickly and accurately locate problematic information and delete it before it is seen is critical to maintaining reputable and secure websites and online content.

Sales organization and business process outsourcing specialist with over 15 years experience in building and running highly efficient sales and customer support organizations, and in providing board and project level consulting to the sales and service organizations of leading companies all over the globe. Developed and implemented staffing strategies and programs that improved operational outcomes and maximized the available staff resources. Specializes in client experience, business process re-engineering, business requirements development, contact center optimization, customer relationship Management, staff training and motivation, and organizational analysis. Has led multiple teams in the successful development and implementation of new business models in BPO industries.

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