An unimaginable volume of content is published every minute, and it’s often hard for brands to keep up. Effective content moderation helps safeguard trust and mitigate risks while reducing costs.
Manually moderating this content is time-consuming and requires a high level of psychological stamina. Automation makes this process faster and ensures that dubious content is flagged and forwarded for human review in real-time.
Pre-moderation
Using pre-moderation, AI algorithms detect and flag content that may violate platform guidelines. This allows human moderators to focus on more challenging and sensitive cases. It also helps to reduce the risk of false positives.
Brands that engage users in two-way interactions like social media, dating apps or e-commerce platforms can benefit from pre-moderation. It identifies inappropriate content like nudity, explicit language and violence that can harm or offend users.
This approach requires a high level of operational precision, meaning that it must be able to identify harmful behavior without misflagging benign content. This is achieved by tuning AI models with feedback from human moderators and ongoing machine learning. It can be used alongside other moderation techniques and is a key element of any trust and safety strategy.
Post-moderation
Post-moderation uses AI to identify content that violates a company’s community guidelines. It combines computer vision and natural language processing to detect offensive content in photos, videos, and text. It also identifies abusive behavior and blocks the offending user’s IP address.
Without a moderation solution, platforms would need to manually examine every user submission for signs of hate speech or other violations. This can be impossible for companies with high volumes of content or those that operate in multiple languages. It is also expensive to hire and train a 24×7 team. Instead, companies can partner with a moderator agency to manage their community and reduce costs. Spectrum Labs’ data vault contains labeled data that teaches models to recognize harmful behaviors and how to flag them.
Object detection
Object detection is the process of identifying illegal or harmful elements in visuals, text, videos and even live streams. Spectrum Labs uses supervised machine learning to train algorithms that recognize specific behaviors, including bullying and harassment, sex and violence, and more.
AI systems do not suffer from mental fatigue and they can see patterns that a human might miss, such as an adult male asking a pre-teen girl about her day at school which could be seen as grooming. This early pattern recognition drives a high detection rate and helps keep community members safe.
The digital world demands speed and a cost-efficient moderation solution. Blending automated with live moderator support allows brands to scale campaigns without adding extra workloads or risking quality. It also saves money by reducing the number of content and behavior violations that require manual review.
Natural language processing
Unlike keyword-based tools, natural language processing analyzes context and semantics. This makes it particularly effective for detecting slang and other culturally specific symbols that would otherwise slip through the cracks of an automated system.
Using this technology, AI content moderation programs can quickly identify illegal and harmful material in images, text, videos, and even live streams. They can then send those items to human teams for review and decision-making. This reduces the workload on moderators and enables platforms to keep pace with the ever-changing world of online behavior.
Companies need to continually refine their AI automated content moderation systems to achieve high operational precision. This can involve adjusting detection thresholds and incorporating new data sources. In addition, user feedback is an important component of this process.
Sentiment analysis
Many online platforms struggle to keep users safe from harmful content. Traditional moderation methods can be costly, time-consuming, and prone to error, which is why AI-based algorithms are increasingly being used to automate the process of flagging, reviewing, and removing harmful content from user-generated data.
Sentiment analysis is a valuable tool for identifying text content that violates community guidelines. Its ability to detect sentiment in a given context provides a powerful alternative to word-based or topical filtering, which can miss nuanced language and contextual clues like sarcasm.
Spectrum Guardian is a powerful and scalable text, image, metaverse, and video moderation solution that uses a combination of ML and AI tools to filter and identify unwanted content across a platform’s community. The solution uses a unique combination of automated and human moderation, with a focus on protecting children online. Its machine learning models are trained on a proprietary data vault that captures harmful behaviors observed on all client platforms.