Our Approach To Dangerous Organizations and Individuals
How we approach dangerous organizations and individuals.
Our approach to the opioid epidemic
How we support communities in the face of the opioid epidemic.
Our approach to elections
How we help prevent interference, empower people to vote and more.
Our approach to misinformation
How we work with independent fact-checkers, and more, to identify and take action on misinformation.
Our approach to newsworthy content
How we assess content for newsworthiness.
Our approach to Facebook Feed ranking
How we reduce problematic content in News Feed.
Our approach to explaining ranking
How we build AI systems.
Content Library and Content Library API
Comprehensive access to public data from Facebook and Instagram
Ad Library tools
Comprehensive and searchable database of all ads currently running across Meta technologies
Other research tools and datasets
Additional tools for in-depth research on Meta technologies and programs
Community Standards Enforcement Report
Quarterly report on how well we're doing at enforcing our policies on the Facebook app and Instagram.
Intellectual Property
Report on how well we're helping people protect their intellectual property.
Government Requests for User Data
Report on government request for people's data.
Content Restrictions Based on Local Law
Report on when we restrict content that's reported to us as violating local law.
Internet Disruptions
Report on intentional internet restrictions that limit people's ability to access the internet.
Widely Viewed Content Report
Quarterly report on what people see on Facebook, including the content that receives the widest distribution during the quarter.
Regulatory and Other Transparency Reports
Download current and past regulatory reports for Facebook and Instagram.
Meta’s technologies detect and removes the majority of violating content before it’s ever reported. When someone posts on Facebook, Instagram, Messenger or Threads, our technologies check to see if the content goes against the Community Standards. In most cases, identification is a simple matter. The post either clearly violates our policies or it doesn’t.
Other times, identification is more difficult. Perhaps the sentiment of the post is unclear, its language is particularly complex or its imagery too context-dependent. In these cases, we conduct further review using people.
Prioritizing factors for human review
When determining which content our human review teams should review first, we consider 3 main factors:
SEVERITY
How likely is it that the content could lead to harm, both online and offline?
VIRALITY
How quickly is the content being shared?
LIKELIHOOD OF VIOLATING
How likely is it that the content in question does in fact violate our policies?
Because we want to prevent as much harm as possible, our review systems use technology to prioritize high-severity content with the potential for offline harm and viral content which is spreading quickly.
Human review teams help our technology improve
Our human review teams use their expertise in certain policy areas and locales to make difficult, often nuanced judgment calls. Every time reviewers make a decision, we use that information to train our technology. Over time, across millions of decisions, our technology gets better, allowing us to remove more violating content.
Technology helps human review teams do what they do best
Like many machine learning models, our technology improves over time as it receives more examples of violating content. This means human review teams have been able to focus more on severe, viral, nuanced, novel and complex content—exactly the sort of decisions where people tend to make better decisions than technology.
Common questions about content review prioritization
How has enforcement prioritization changed over time?Previously, human review teams would spend the vast majority of their time reviewing content reported by people. This meant they were often spending too much time on low-severity or clearly non-violating content and not enough time on the severest content with the greatest potential for harm. It also meant many human decisions weren’t that useful for improving our enforcement technology.
Our current approach to prioritization addresses these issues, allowing us to review the most potentially harmful content first and improve our technology faster.
Do human review teams review every user report?Not necessarily. Both human review teams and technology play a role in reviewing user reports. In cases where our technology can analyze a given piece of content, it will automatically take action—or not—on the content in question.
How do you ensure that human decisions don’t introduce bias into your technology?To address fairness and inclusion concerns associated with the deployment of AI in Meta technologies, we created our Responsible AI team—a dedicated, multidisciplinary team of ethicists, social and political scientists, policy experts, artificial intelligence researchers and engineers. The team’s overall goal is to develop guidelines, tools and processes to tackle issues of AI responsibility and help ensure these systemic resources are widely available across Meta.
