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CONTENT MODERATION SOLUTIONS: TO GUARD THE QUALITY OF DIGITAL DATA

CONTENT MODERATION SOLUTIONS:

Content moderation is when an online platform reviews and monitors user-generated content based on platform-specific rules and guidelines to determine if the content needs to be published on the online platform. That is when a user submits content to a website, the content goes through a review process (moderation process) to ensure that the content complies with the website’s regulations and is not illegal, inappropriate, or harassing. Moderation is a common content practise on online platforms that rely heavily on user-generated content, such as social media platforms, online marketplaces, sharing economies, dating sites, communities, forums, and others. Pre and post moderation, reactive moderation, distributed moderation, and automatic moderation are all examples of content moderation. Content Moderation Solutions serves a wide range of business verticals, which is propelling the Content Moderation Solution globally.

Advantages of Content Moderation Solutions

Content moderation is needed for a business that has a large presence online representing its brand globally. Large audience and brand value create an influential wave by brands to its customers. Therefore, such big enterprises need to be careful to make any statements and posts regarding a sensitive issue that can hurt the sentiment of the large audience and also need to filter the associated online presence of such events. Large enterprises can use Content Moderation Solutions to monitor, filter, and moderate content posted to or by them. Leading corporations such as Microsoft, Alphabet, IBM, and Accenture offer an integrated software solution for individual businesses as well as customised government solutions. Currently, the majority of software is AI-powered, resulting in quick action and the most accurate solution. Some of the leading software in the Content Moderation Solutions Market include Amazon Rekognition, Mobius Labs, WebPurify, ModerateContent, and Azure Content Moderator.

AI Moderation or tailored AI Moderation is a machine learning model created from platform-specific online data that efficiently and accurately capture unwanted user-generated content.  AI moderation solutions make highly accurate and automated moderation decisions that automatically reject, approve, or escalate content. AI Moderation is used in ‘Anibis’ which is a Swiss online marketplace, which has successfully automated 94% of the moderation and achieved an accuracy of 99.8. As long high-quality dataset to build your model, AI moderation is ideal for everyday decision-making. In most cases, it is good at handling cases that look the same or very similar. This typically covers the majority of the articles published on the online marketplace, so AI moderation can benefit most platforms. It should also be mentioned. General data can be used to moderate AI. While these models are useful, they are not as accurate as custom AI solutions because they do not consider.

The need for content moderation depends on the type of content. Comment moderation, image or video moderation, and mood moderation will only increase if the amount of content uploaded online surges at an unprecedented rate. Most social media companies have implemented strict community guidelines to set the criteria for the types of content that can be published on these platforms, and more and more companies in the content moderation solution market are finding effective content. Publishing user-generated content carries risks which companies can use a scalable content moderation process to publish large amounts of user-generated content while protecting their reputation, customers, and revenue. Content moderation protects both brands and users. There is always the possibility that some user-generated content, such as contest videos, images on social channels, blog posts, and forum comments, will deviate from what the brand considers acceptable. In this case, a content moderation solution is a viable option for mitigating the situation. Most people rely on information obtained from the Internet as their use of technology grows. Customers expect to find your correct location, an updated catalogue of your products and prices, and a contact number when they search for you online. Content Moderation Solutions can handle inappropriate comments on the brand’s online platform.

New Trend in Content Moderation Solutions

Integration of AI

In reality, there are too many UGCs to keep up with human moderators, and companies are required to support them effectively. AI automation supports human moderators by accelerating the review process. As the amount of content users generate grows, AI can be used by enterprises to quickly scale to the resources available. Being able to find and remove inappropriate content faster and more accurately is paramount to maintaining a trusted and secure community website. The challenge for many businesses is to quickly identify and remove toxic content before it causes any problems. Artificial intelligence-powered content moderation enables online businesses to grow faster and optimise content moderation in a more consistent manner for users. It does not, however, eliminate the need for human moderators to provide ground truth monitoring for accuracy and to address sensitive content concerns in context. Different types of content require different techniques to moderate such as:

Image moderation – Image moderation uses text classification and computerized visual search techniques. These techniques use a variety of algorithms to detect harmful image content and locate it in the image. Image moderation uses image processing algorithms to identify different areas of the image and classify them according to specific criteria.  If the image contains text, Optical Character Recognition (OCR) can also recognize and moderate the text. These techniques help identify offensive or offensive words, objects, and body parts in all kinds of unstructured data.

Video moderation – Video moderation uses computer vision and artificial intelligence techniques. Unlike moderated images, where inappropriate content appears immediately on the surface, moderated video requires you to see the entire video or see it frame by frame. End-to-end moderation requires a complete review of the video to validate both audio and visual content. Still, moderation requires taking records at multiple intervals, using computer vision techniques, and then reviewing those records to ensure that the content is appropriate.

Text moderation – Use of natural language processing algorithms to summarize the meaning of the text and understand the emotions of the text. Text classification allows assigning categories to analyze text or emotions based on content. Sentiment analysis identifies the tone of the text, classifies it as angry, bullying, ironic, etc., and then marks it as positive, negative, or neutral. Another commonly used technique is called entity recognition. Automatically search and extract names, locations, and companies. For example, users can track how often a brand is mentioned in online content, how often it is mentioned by competitors, and even how many people have posted reviews for a particular city or state. More advanced techniques include moderating text in the knowledge base which is used as a built-in database to make predictions about the adequacy of your text.

Policy Implications

Availability of online content moderation services to encourage third-party providers. It helps to make sure platforms of all sizes have access to the service encourage the use of AI and automation technology to improve the performance and effectiveness of your content moderation.

Sharing records to identify harmful things content between the platform and the moderation service provider shall help and be encouraged to maintain standards. Data trust can provide a suitable framework related to data held by public organizations such as the BBC which can contribute to the interests of society to make a comprehensive dataset available and keep up to date the categories of malicious content and formats which are evolving at a fast pace. (UK Jurisdiction)

It is important to build that potential public trust the causes of bias in AI-based content moderation which are understood and appropriate steps are taken to mitigate them. This can be done by examining and adjusting the records to do understand how diverse they represent by setting up a test regime for individuals in society for an AI-based content moderation system.

To ensure the right level of protection for internet users, it is important to understand the performance of AI-based content moderation through moderation services across individual platforms and all categories. This is to ensure that they are properly mitigated and develop further with the expectations of society and the sensitivities of their respective countries or cultures.

Key Software

Amazon Rekognition

ModerateContent

Microsoft Azure

WebPurify

Mobius Labs

CrowdSource

Community Sift

Two Hat

Lionbridge AI

ICUC.Social

Conclusion

As the digital industry moves forward with a technological revolution like AI, MI, and Big Data having tremendous implications in various business verticals. The mass population showed a digital presence in the last five years, creating brands to capitalize on such opportunities to make businesses digital. The exchange of transactions, as well as communication between consumers and business entities as well as third parties, shows the bone and bane of digitalization. With the positive impact, there is a high need for moderation of text, videos, and images to keep the digital space civil. Transparency and consistency in implementing community guidelines should be supported by trust and security teams who frequently evaluate tools and solutions. Content moderation can help make your community a better place, whether it’s protecting your audience, increasing brand loyalty and user engagement, or maximizing moderator productivity. Therefore, content moderation solution holds a bright future to provide secure digital space for both ends of the spectrum.

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