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.
This metric shows the percentage of all content or accounts acted on that we found and actioned before users reported them to us. We use this metric as an indicator of how effectively we detect violations.
Our investments in machine learning technology are critical to help us detect faster.
We balance machine learning with a trained team of experts who review and take action on violating content.
The rate at which we can proactively detect potentially violating content is high for some violations, meaning we find and action most content before users report it to us. This is especially true where we’ve been able to build machine learning technology that automatically identifies content that might violate our standards.
Such technology is very promising but is still years away from being effective for all kinds of violations. For example, there are still limitations in the ability to understand context and nuance, especially for text-based content. This creates additional challenges for proactively detecting certain violations.
The metric can go up or down due to external factors. For example, a cyberattack during which spammers share 10 million posts featuring the same malicious URL. If we detected the malicious URL before any user reported it to us, proactive rate would go up during the cyberattack and go down afterward, even if our detection technology didn’t change during the period. This metric can also increase or decrease based on how our processes and tools change — for example, it might go up if our detection technology gets better, but it might go down if our user reporting improves and we rely less on proactive detection.
Since this metric is based on the amount of content actioned, many of the same considerations apply. Our proactive rate doesn’t reflect how long it takes us to detect violating content or how many times it was viewed before detection. It also doesn’t reflect how many violations we failed to detect altogether or how many times that content was viewed. And though the percentage of content we proactively detect can be very high — as high as 99% in some categories — even the remaining small percentage can cause significant impact to people.
How we calculate our proactive rate
We calculate this percentage as: the number of pieces of content acted on that we found and actioned before people using Facebook or Instagram reported them, divided by the total number of pieces of content we took action on.
For fake accounts on Facebook, we calculate this metric as the percentage of Facebook accounts disabled for being fake that we found and actioned before users reported them to us. It’s calculated as the number of disabled accounts we found and actioned before users reported them, divided by the total number of accounts disabled for being fake.
Caveats
We compute our proactive rate using a strict attribution of user reports to content. For example, if someone reports a Page and, while reviewing the Page, we identify and act on some violating content within that Page, we would report that we had actioned that content proactively (unless there were specific additional user reports of it).
