Several tools exist that help Trust & Safety teams moderate user-generated content. These tools fall into three categories: word filters and RegEx solutions, classifiers, and contextual AI.
Once a model is set up, it should be monitored and tuned regularly to keep its accuracy high. This is done through active learning loops of customer feedback, moderator actions, and new training data.
Machine learning uses predictive algorithms to analyze and monitor user-generated content on online platforms. This technology can be used to augment or replace manual moderation workflows at the pre-moderation stage, identifying harmful behavior and prioritizing it for review by human moderators.
When used effectively, this allows businesses to save time and resources by automating content moderation processes while allowing moderators to focus on more pressing and valuable community growth oriented tasks. It also enables brands to keep their communities safe and healthy by quickly responding to submissions that violate community guidelines.
However, it’s essential for marketers to understand the limitations and risks associated with implementing content moderation AI tools within their marketing strategies. This includes identifying and understanding what makes an effective AI system in terms of operational precision (i.e. avoiding false positives/negatives and handling context like sarcasm). The best content moderation AI tools will have a high level of operational precision and will regularly undergo active tuning cycles with human feedback.
Natural Language Processing
Artificial intelligence algorithms that use natural language processing can improve content moderation by automating classification and flagging. These tools can identify keywords and determine whether a word or phrase is inappropriate or harmful. This process is faster and more consistent than human moderation, but it can still miss some things.
These algorithms can also detect emotions, including hate speech and violence, and understand sarcasm and other idioms. They can also analyze the context of a piece of text and detect any emojis or symbols that might be hiding meaning.
However, it is difficult to build an AI model that can identify all instances of a particular behavior. This can lead to issues with censorship (such as clause 9 of the proposed Online Safety Bill), or a lack of understanding of cultural contexts. Spectrum Labs’ approach to this problem is to focus on a single domain, such as Trust & Safety, and actively tune models using customer feedback and moderator actions (e.g., de-flagging a piece of text that was incorrectly flagged for profanity) and by updating language models to take into account emerging slang or connotations.
As user-generated content (UGC) becomes more prevalent in the digital era, businesses are looking for efficient ways to moderate this material. AI tools have become a popular choice for this purpose. These tools use a variety of technologies, including entity recognition technology, computer vision algorithms to detect explicit images and videos, and natural language processing techniques for evaluating voice recordings.
These tools help to identify harmful content and flag it for review by human moderators. They also use active learning, which involves customer feedback and moderator actions (such as de-flagging a piece of text that was mistakenly flagged as profanity) to refine their models.
Whether used in pre-moderation or post-moderation, these tools can improve moderation accuracy by identifying patterns of potentially harmful behavior. This can also reduce the time spent by humans reviewing this material.
Whether it’s text, audio or video, AI content moderation is able to quickly assess the tone and sentiment of user-generated content. This allows for a faster, more accurate assessment of potentially harmful content and helps to keep community members safe. For example, an AI system can identify early pattern recognition of a harmful relationship (such as an adult male asking a pre-teen girl what she wore to school that day) much quicker than a human moderator might.
Spectrum Labs’ Guardian AI platform uses multiple types of machine learning and NLP to improve the accuracy and speed of its content moderation processes. However, it’s important to understand the limitations of these tools such as false positives/negatives, handling context or sarcasm and potential bias in algorithms. This is why active training cycles and a team of dedicated human moderators are required to monitor the performance of these systems. They can then update policies to close any gaps or grey areas that may arise.