When user-generated content (UGC) hits platforms, it needs to be screened for harmful and invasive material. AI can help automate this process and ensure a high level of quality and security for users.
However, it is important to note that AI moderation has its blindspots. The major one is qualitative judgment. Human moderators are better equipped to judge the intent behind a piece of content, something that automated filters are unable to do.
AI-powered pre-trained systems
Automative content moderation is a powerful way to keep the Internet safe from harmful content. It enables brands to monitor all user-generated content before it goes online, helping them to avoid censorship and protect their brand image.
AI-powered pre-trained systems can help to improve the efficiency of the moderation process. They can be used to filter user-generated content (UGC) with a high degree of accuracy, as well as flag content for human review before it goes live.
Developing accurate models for automated content moderation requires a lot of data, especially if you want to build a system that can recognize dozens of languages, cultural standards and overall contexts. It also helps to update your model regularly with new data, as language changes over time.
Moreover, algorithms can be biased when they are created and trained, so they should only be used in an ethical way that doesn’t further stigmatize or silencing already disadvantaged populations. This is the reason why it’s important to ensure that your algorithms are transparent and follow international human rights law.
With 720,000 hours of video and billions of images uploaded to social media platforms every day, trust and safety teams are often overwhelmed by the amount of content that needs moderation. Automated systems can be a big help, but the burden of manually screening content remains a crucial part of ensuring that user-generated content is safe and appropriate on the platform.
AI can be used in a number of ways to help automated content moderation. It can flag inappropriate content according to a set of guidelines, which can then be forwarded to human moderators for manual review.
It can also be used to re-assess flagged content to determine which content requires further attention from human moderators. This can save both time and frustration for human moderators, while reducing the amount of inappropriate content on the platform.
Computer vision uses machine learning techniques to recognize objects in pictures and videos. Using a convolutional neural network, computer vision algorithms learn how to identify key features, resulting in high accuracy. This technology is increasingly being used in applications beyond just the internet, from enhancing selfies to detecting lung lesions in medical images.
Natural language processing
Natural language processing (NLP) enables computers to understand what humans are saying. This includes text, images and videos.
AI-powered content moderation technologies can spot and remove offensive and harmful online content. They can also help companies identify fraudulent and misleading posts that may be used to manipulate public opinion or sell products.
NLP algorithms are trained on hundreds of texts written in over a hundred languages, and they can also analyze the tone and emotion behind words and phrases. Moreover, they can search for specific keywords within the text and predict whether it adheres to moderation policies.
Automated systems can be used for many different tasks in moderation, including keyword filtering, image and video analysis and sentiment analysis. These tasks can be used alone or in combination to tailor your AI-powered content moderation process to your company’s needs.
When used correctly, sentiment analysis can help businesses gain a more comprehensive understanding of their customers and their opinions. It is a powerful tool for brand monitoring, product review management, and customer support.
Sentiment analysis systems gather insights from unstructured text sources that are spread across a range of channels, including emails, blog posts, support tickets, online forums and social media. The systems can use rule-based, automatic or hybrid methods to extract data.
Most sentiment analysis systems use lexicons and complex machine learning algorithms to identify positive, negative, neutral or neutral polarity, as well as emotions. Emotion detection is a type of fine-grained sentiment analysis that goes beyond polarity to identify specific feelings and emotions like happiness, anger, frustration or sadness.