As social media platforms become more popular, so do the issues associated with them. One issue is that online communities can be used to spread hate speech and violence.
AI tools can help moderate this content by using a number of techniques. Robust moderation AI solutions, like Spectrum Labs Guardian AI, include word filters and RegEx solutions, classifiers, and context-based detection algorithms.
The most common form of content that can taint a platform or brand is text. This is why most automated content moderation tools rely on word filters and RegEx solutions, classifiers, or contextual AI to identify problematic behavior.
Unfortunately, these tools are limited in several ways. For instance, they are often inaccurate when identifying toxic content. They are also prone to a false positive rate and may miss implied connotations in certain contexts.
A robust AI model requires active learning and “human in the loop” tuning cycles. This includes customer feedback, moderation actions (de-flagging an incorrectly flagged piece of content), and updates to language models to account for new slang and hidden connotations.
In addition, a robust AI model needs to be highly accurate and provide a high level of operational precision. Operational precision measures how often a model correctly identifies harmful behavior while also minimizing the number of benign behaviors it accidentally flags. Spectrum Labs patented multilingual AI provides this capability with native support for character-based and hybrid languages, l33tspeak, and emojis.
In order to sift through images, content moderation uses image recognition to identify harmful or inappropriate material. Image recognition has two components, which include visual question answering and computer vision. Visual question answering involves asking the image to answer questions, whereas computer vision analyzes the image to flag potentially harmful content. This reduces the impact on human moderators as they do not have to view the actual images.
User-generated content (UGC) can take many forms, including text, images, video and audio. It is crucial for any community that allows UGC to have a system in place for moderation.
Typically, community members are responsible for reporting any content that is considered harmful. It is then reviewed by a human or AI and deleted if necessary. Eden AI’s image moderation API includes pre-trained modules for NSFW content, explicit nudity and violence detection. The API can also be customized for your unique needs. It can be integrated into your existing platform or workflow to support mission critical use cases like:
With user-generated content dominating the digital world, ensuring a safe space for users on your online community requires strict moderation guidelines. This includes monitoring a large volume of images and videos.
Image and video moderation AI uses computer vision algorithms to analyze uploaded photos or videos for harmful or inappropriate content. This can include nudity, violence, gore and hate symbols. It can also detect text within an image, such as street signs or T-shirt slogans, and use natural language processing to identify the context of the text.
When an image or video has been flagged, the Guardian solution sends a webhook to your API with a determination and recommended response. This helps to streamline the process and ensure a consistent approach to moderating content. It also helps to reduce the amount of time human moderators spend viewing distressing content, and decrease their exposure to harmful material that can cause negative psychological effects. This allows them to focus on other aspects of the work.
While catching nude images and inappropriate text on social media are challenging, catching hate speech or other offensive behaviour in live audio is even more complex. It requires a highly trained team of digital first responders, who understand community guidelines and regional laws.
Effective audio moderation involves a hybrid approach that leverages the best of both humans and AI. While AI can catch clear-cut cases and flag them quickly, it is not as good at detecting subtle language like implied tone, regional dialects and slang or how the same words may mean something different in different locales and subcultures.
To tackle these challenges, a livestream audio moderation solution must capture and transcribe the audio, turning it into text. This is scanned using WebPurify’s profanity and intent filters, before moderators review the results. This allows human moderators to proactively address issues, rather than reactively responding to reports. This reduces the risk of harm to users and protects brand reputation.