11/13/2019 | Press release | Distributed by Public on 11/14/2019 03:09
Today we're publishing the fourth edition of our Community Standards Enforcement Report, detailing our work for Q2 and Q3 2019. We are now including metrics across ten policies on Facebook and metrics across four policies on Instagram.
These metrics include:
We also launched a new page today so people can view examples of how our Community Standards apply to different types of content and see where we draw the line.
Adding Instagram to the Report
For the first time, we are sharing data on how we are doing at enforcing our policies on Instagram. In this first report for Instagram, we are providing data on four policy areas: child nudity and child sexual exploitation; regulated goods - specifically, illicit firearm and drug sales; suicide and self-injury; and terrorist propaganda. The report does not include appeals and restores metrics for Instagram, as appeals on Instagram were only launched in Q2 of this year, but these will be included in future reports.
While we use the same proactive detection systems to find and remove harmful content across both Instagram and Facebook, the metrics may be different across the two services. There are many reasons for this, including: the differences in the apps' functionalities and how they're used - for example, Instagram doesn't have links, re-shares in feed, Pages or Groups; the differing sizes of our communities; where people in the world use one app more than another; and where we've had greater ability to use our proactive detection technology to date. When comparing metrics in order to see where progress has been made and where more improvements are needed, we encourage people to see how metrics change, quarter-over-quarter, for individual policy areas within an app.
What Else Is New in the Fourth Edition of the Report
Progress to Help Keep People Safe
Across the most harmful types of content we work to combat, we've continued to strengthen our efforts to enforce our policies and bring greater transparency to our work. In addition to suicide and self-injury content and terrorist propaganda, the metrics for child nudity and sexual exploitation of children, as well as regulated goods, demonstrate this progress. The investments we've made in AI over the last five years continue to be a key factor in tackling these issues. In fact, recent advancements in this technology have helped with rate of detection and removal of violating content.
For child nudity and sexual exploitation of children, we made improvements to our processes for adding violations to our internal database in order to detect and remove additional instances of the same content shared on both Facebook and Instagram, enabling us to identify and remove more violating content.
While we are including data for Instagram for the first time, we have made progress increasing content actioned and the proactive rate in this area within the last two quarters:
For our regulated goods policy prohibiting illicit firearm and drug sales, continued investments in our proactive detection systems and advancements in our enforcement techniques have allowed us to build on the progress from the last report.
New Tactics in Combating Hate Speech
Over the last two years, we've invested in proactive detection of hate speech so that we can detect this harmful content before people report it to us and sometimes before anyone sees it. Our detection techniques include text and image matching, which means we're identifying images and identical strings of text that have already been removed as hate speech, and machine-learning classifiers that look at things like language, as well as the reactions and comments to a post, to assess how closely it matches common phrases, patterns and attacks that we've seen previously in content that violates our policies against hate.
Initially, we've used these systems to proactively detect potential hate speech violations and send them to our content review teams since people can better assess context where AI cannot. Starting in Q2 2019, thanks to continued progress in our systems' abilities to correctly detect violations, we began removing some posts automatically, but only when content is either identical or near-identical to text or images previously removed by our content review team as violating our policy, or where content very closely matches common attacks that violate our policy. We only do this in select instances, and it has only been possible because our automated systems have been trained on hundreds of thousands, if not millions, of different examples of violating content and common attacks. In all other cases when our systems proactively detect potential hate speech, the content is still sent to our review teams to make a final determination. With these evolutions in our detection systems, our proactive rate has climbed to 80%, from 68% in our last report, and we've increased the volume of content we find and remove for violating our hate speech policy.
While we are pleased with this progress, these technologies are not perfect and we know that mistakes can still happen. That's why we continue to invest in systems that enable us to improve our accuracy in removing content that violates our policies while safeguarding content that discusses or condemns hate speech. Similar to how we review decisions made by our content review team in order to monitor the accuracy of our decisions, our teams routinely review removals by our automated systems to make sure we are enforcing our policies correctly. We also continue to review content again when people appeal and tell us we made a mistake in removing their post.
Updating our Metrics
Since our last report, we have improved the ways we measure how much content we take action on after identifying an issue in our accounting this summer. In this report, we are updating metrics we previously shared for content actioned, proactive rate, content appealed and content restored for the periods Q3 2018 through Q1 2019.
During those quarters, the issue with our accounting processes did not impact how we enforced our policies or how we informed people about those actions; it only impacted how we counted the actions we took. For example, if we find that a post containing one photo violates our policies, we want our metric to reflect that we took action on one piece of content - not two separate actions for removing the photo and the post. However, in July 2019, we found that the systems logging and counting these actions did not correctly log the actions taken. This was largely due to needing to count multiple actions that take place within a few milliseconds and not miss, or overstate, any of the individual actions taken.
We'll continue to refine the processes we use to measure our actions and build a robust system to ensure the metrics we provide are accurate. We share more details about these processes here.