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.
The challenge of detecting hate speech
UPDATED
JAN 19, 2022
Hate speech is especially difficult for technology and human review teams to detect. Idioms and nuances vary widely across cultures, languages and regions. Also, people sometimes share words that would normally be hate speech, but they do it to raise awareness for the problem or to use self-referentially in an effort to reclaim the term.
Those are challenges just detecting hate speech in text. A lot of hate speech we find on Facebook and Instagram is in photos or videos. A meme, for example, might use text and images together to attack a particular group of people. This is an even greater challenge for technology.
Content like this gets more complicated when people try to avoid detection by changing their content. For example, they might misspell words, avoid certain phrases or modify their images and videos.
Progress in using artificial intelligence to detect hate speech
We improved our tools for detecting hate speech over the last several years, so now we remove much of this content before people report it—and, in some cases, before anyone sees it.
We use AI to identify images and text that are identical to content that we already removed as hate speech. Our technology also looks at reactions and comments to assess how similar a piece of content is.
These techniques help our technology more accurately detect hate speech, even when the meaning is not obvious or the content is changed to avoid detection.
