Content moderators review batches of items — either textual or visual — and mark those that don’t comply with a platform’s guidelines. This is often a slow process that can be dangerous if not assisted by automatic pre-screening.
Monitoring large volumes of images, videos and live streamed footage is not a job that human moderators can do alone. This is where video content moderation technology comes in.
Voice Analysis
Using voice analysis as part of your video content moderation software, you can transcribe audio and run natural language processing to find potentially harmful content. This form of AI can help you identify and moderate hate speech, discrimination, sexism, trolling and bullying.
Founded in 2018, Sightengine’s state-of-the-art deep learning tech is used for image, video and text moderation. Their scalable platform is simple to use and helps create positive user experiences.
They work with large global brands and have a wide range of services for companies of all sizes. They help reduce the amount of time moderators spend searching for videos, making it more efficient and effective.
Trusted and loved by brands like Roblox, CommunitySift uses an AI-powered moderation tool to combat online harassment and cyberbullying. Their tech filters and escalates over 100 billion interactions per month in real time. This includes usernames, photos and videos in more than 50 languages. It also detects offensive language, age-inappropriate material and flags inappropriate content.
Text Analysis
When it comes to text-based content moderation, sentiment analysis is used to decipher the tone and intent of a piece of writing. Using natural language processing algorithms, text analysis identifies emotions and determines whether the intended message is bullying, anger, or sarcasm. Other features like named entity recognition and text classification help to identify specific words, locations and companies and categorize them accordingly.
Text analysis helps to mine insights from unstructured data that businesses gather through various sources like social media, transaction records and customer feedback surveys. This data may include text, images or videos that are hard to understand or make sense of without the help of artificial intelligence and machine learning.
One of the most popular examples of ML content moderation is Facebook’s use of digital hash technology that allows them to flag extremist content on their platform and remove it in real time. This is a result of their scalable and automated approach to filtering and identifying unwanted or harmful content.
Computer Vision
As a content moderation technology, computer vision uses machine learning to recognize and classify images and video. It helps identify harmful content and flag it for review. The process involves using algorithms to scan and analyze a large number of image files and videos in real-time. This allows moderators to quickly and accurately moderate content without consuming significant amounts of human resources.
ML-based computer vision software can automatically detect inappropriate or offensive images, such as those showing explicit nudity, drugs, and violence. It can also transcribe and understand text emblazoned on images or in videos, and even detect copyrighted or trademarked designs.
Using computer vision in tandem with voice analysis and text processing can help reduce false positives, as it will take into account the context of a video to determine whether it is harmful or not. This way, it won’t censor videos simply because one or more algorithmic rules have been triggered. It will also help to prevent emotional fatigue that can occur when a human moderator has to continuously view upsetting or offensive material.
Artificial Intelligence
Billions of people share text, image and video content online, which can be inappropriate, obscene, or even illegal. Manually scanning through this digital trash requires human content moderators to spend time away from their work, and the job can take a psychological toll.
Using AI for automated moderation enables businesses to create safer, more responsible digital spaces for users. It also allows companies to increase user engagement, improve brand image and grow revenue.
Developing a responsible AI strategy that involves all stakeholders and clearly defines use cases, quantifies benefits and risks, aligns business and technology teams and changes organizational competencies will help build trust in your company’s AI systems. In addition, a clear process for modeling, testing and deploying AI is essential. This will ensure transparency and compliance with regulatory requirements. It will also help you manage and protect your data assets. For example, you need to establish who owns the model and the underlying data used in your AI and how that is controlled and audited.