When people talk about automation, the work of online content moderation is often overlooked. But this is a crucial frontline job. These employees spend their days in sterile, call center-style offices, policing the worst of human behavior one disturbing picture or video at a time.
This report takes a closer look at this essential work and the challenges that come with it.
Pre-moderation
Using pre-moderation, each piece of content is checked and reviewed manually by a moderator before it goes live. This is often the best solution for sites that are time sensitive and/or aimed at children. It can help avoid offending visitors and maintain good site reputation.
In a world where user generated content is increasingly influencing customer decisions, UGC moderation is an essential tool for businesses. The right moderation strategy will ensure that your users’ posts are safe, relevant and in line with your community guidelines.
AI can make moderation faster and more efficient by screening for inappropriate content before it reaches your users. This includes identifying specific words or phrases and applying natural language processing to determine their intent. It also uses computer vision and image recognition to identify potentially harmful images and read captions or metadata. It can also perform voice analysis to evaluate the tone and content of recorded messages.
Post-moderation
As the popularity of user-generated content continues to grow, brands must find ways to monitor it. Otherwise, their digital platforms may grind to a halt or become cluttered with illegal and harmful content. Moderation automation is one solution. It allows brands to moderate content in real-time and removes it instantly, providing their customers with a safe online experience.
Depending on the platform, moderation automation can scan images and text or check videos on live stream. This is a great solution for sites that need to respond quickly, such as online communities and marketplaces for time-sensitive products like concert tickets.
However, the primary limitation of these tools is their inability to understand context. For example, AI can detect keywords such as “drug,” but may miss the fact that a person is referring to a drug prevention campaign. It also struggles to recognize cultural symbols such as emojis. Adding more details to a base model can help mitigate these limitations.
Distributed moderation
This moderation technique relies on members of an online community to review and flag any content that may be inappropriate or harmful. This is done through a ‘report’ button that can be triggered on any submission. After this, human or automated content moderation AI reviews the flagged content and removes it from the platform if needed.
Distributed moderation is a great option for brands that want to empower their human moderators with a feature-packed automatic solution. It helps them sift through large amounts of user-generated content faster and with more accuracy. However, it still leaves room for error and can lead to damaging content remaining public for too long before it’s taken down. That’s why it is important to use a combination of manual and automated processes, such as the one offered by Imagga. This tool combines artificial intelligence and natural language processing to filter out any content that is inappropriate or harmful. It also allows users to set their own thresholds for specific types of visual content.
Human moderation
As user-generated content continues to grow on digital platforms, it’s critical that human moderation be able to identify problematic posts quickly and accurately. This process is referred to as “human-in-the-loop” moderation, where human moderators review content and provide feedback to AI algorithms to improve their performance over time. Human moderation is also a crucial aspect of ensuring that users feel safe and comfortable using online platforms.
Human moderation involves reviewing and removing content that violates community guidelines. This can include images, text, video, and audio. Human moderation also focuses on detecting nuanced tones in language and cultural context, which can be difficult for automation systems to understand.
Human moderators work in sterile call center-type offices and often receive their work through third-party contracting companies and digital piecework platforms like Mechanical Turk. These workers are on the frontlines of the internet, sifting through the worst of humanity’s intentions one disturbing image or video at a time.