More and more people are exploring various online platforms that allow for uploading user-generated content. Every day millions are uploading content either in the form of blogs, images, or videos to online platforms. Some are even making a living out of user-generated content and have made online platforms an integral part of their lives. But unlike the moderation rules associated with traditional offline media, the content on the internet is not subject to any editorial controls. This means users can post content that is cruel and insensitive to others especially children, pornographic content, or the ones that promote violence or terrorism.

There has arisen a need for moderation of content that goes live every other day over the internet. And the onus falls on the online platforms to review the content and flag and remove the inappropriate ones. These platforms are employing thousands of content moderators to vet any new content that is uploaded online. Globally, more than 100,000 people are moderating online content. Facebook for instance has employed 7500 moderators who moderate the content uploaded on their platform based on the rules set by the company.

What is the need for AI in Content Moderation?

The pace at which the user-generated content has been uploaded to the online platforms has made it difficult to identify and remove harmful content using the traditional human-based moderation. AI-based automation systems can assist humans in online content moderation and offer the scale and speed required to match the pace at which online content has been uploaded. This has been possible with the recent advancements in AI along with the availability of data and low-cost computational power needed to create new and improved algorithms.

AI-based moderation systems follow two approaches – content-based and context-based.

Content-based moderation systems can review text, image as well as videos. Named Entity Recognition, an important technique in Natural Language Processing is used for recognizing harmful content such as fake news, hate speech, harassment, etc. While sentiment analysis is used for classifying and labeling content based on the level of emotions involved. Semantic Segmentation, object detection – techniques of computer vision are used for analyzing images and videos.

Context-based moderation involves making the AI learn to understand the context or in simpler terms reading between the lines from various sources.

AI-Based Online Content Moderation Challenges

AI is aiding humans in online content moderation and helping to improve the pace at which content is moderated daily. But still, there are certain challenges that the machines have to overcome to perform efficiently and accurately in the long run.

There is a broad range of content that can be classified as harmful content ranging from child abuse content to spam, insensitive, violent and graphic content, extreme content, hate speech, and others. Some of these can be identified from the content alone while most of it requires the need to understand the context. A wide range of factors such as cultural, societal, political, and historical factors play a role in understanding the context and these contextual considerations vary as per the law of the land and what societies deem as acceptable. So, interpreting the context consistently is a challenge for AI-based systems.

Role of humans in training AI on content moderation

Since context plays an important role in moderating the content online, the role of training the AI-based system to read between the lines has fallen on humans. The rise of human data labelers has aided in the development of AI-based automated content moderation systems. Humans curate and organize the data as part of the data labeling process. They will first comb through the data and label what is appropriate and flag that’s inappropriate content. This helps to train the machines to recognize harmful content and process and moderate billions of user-generated content on online platforms. For AI to moderate content effectively, a mix of human data labelers and moderators is the need of the hour.

This is where data labeling companies like Data Labeler come into the picture. With 1000+ human data labelers working around the clock, we provide the labeled data to help train your AI-based systems for content & context-based moderation. Our team of data labelers will label the data as per your set specific guidelines and objectives to meet your company’s standards and policies. Contact us now for high-quality training datasets required for developing contextually aware AI-based moderation systems.