By Guy Rosen, VP Integrity

Today, we’re publishing our third Community Standards Enforcement Report, covering Q4 2018 and Q1 2019. This report adds a few additional data points:

What’s New in the Third Edition

In total, we are now including metrics across nine policies within our Community Standards: adult nudity and sexual activity, bullying and harassment, child nudity and sexual exploitation of children, fake accounts, hate speech, regulated goods, spam, global terrorist propaganda and violence and graphic content.

How to Make Sense of the Numbers

The key metrics in our report, which together track how we are doing at enforcing our Community Standards, are:

Prevalence: The frequency at which content that violates our Community Standards was viewed. We care most about how often content that violates our policies is actually seen by someone. While content actioned describes how many things we took down, prevalence describes how much we haven’t identified yet and people may still see. We measure it by periodically sampling content viewed on Facebook and then reviewing it to see what percent violates our standards.

This metric is currently available for adult nudity and sexual activity, for violence and graphic content, and for fake accounts:

In this report, we’re also sharing a prevalence metric for global terrorism and for child nudity and sexual exploitation for the first time. The prevalence for both areas is too low to measure using our standard mechanisms, but we are able to estimate that in Q1 2019, for every 10,000 times people viewed content on Facebook, less than three views contained content that violated each policy.

We continue to develop prevalence metrics for the policy areas we include in the report. You can learn more about how we measure prevalence here.

Content Actioned: How much content we took action on because it violated our Community Standards. Our actions include removing the content, applying a warning screen to the content, or disabling accounts. This metric reflects how much content people post that violates our policies, and how well we can identify it.

For fake accounts, the amount of accounts we took action on increased due to automated attacks by bad actors who attempt to create large volumes of accounts at one time. We disabled 1.2 billion accounts in Q4 2018 and 2.19 billion in Q1 2019. We’ll continue to find more ways to counter attempts to violate our policies and Alex Schultz explains more about how we address fake accounts in a Hard Questions blog we’ve also shared today.

Proactive Rate: Of the content we took action on, how much was detected by our systems before someone reported it to us. This metric typically reflects how effective AI is in a particular policy area.

In six of the policy areas we include in this report, we proactively detected over 95% of the content we took action on before needing someone to report it. For hate speech, we now detect 65% of the content we remove, up from 24% just over a year ago when we first shared our efforts. In the first quarter of 2019, we took down 4 million hate speech posts and we continue to invest in technology to expand our abilities to detect this content across different languages and regions.

New This Report: Appeals and Correcting Our Mistakes

When we take action on a piece of content, we notify the person who posted it and in most cases offer them the ability to tell us if they think we made a mistake. In the third edition of the report, we have begun publishing data on how much content people appealed and how much content was restored after we initially took action.

Our enforcement isn’t perfect and as soon as we identify a mistake, we work to fix it. That’s why we are including how much content was restored after it was appealed, and how much content we restored on our own — even if the content wasn’t directly appealed. We restore content without an appeal for a few reasons, including:

We are including this information for Q1 2019 across each policy area in the report except for fake accounts:

* Due to rounding, the amount of content restored after appeal and without appeal may not exactly add up to the total amount of content restored.

The amount of content appealed in a quarter cannot be compared directly to amount of content restored within that quarter, as content restored in Q1 2019 may have been appealed in Q4 2018. Similarly, the amount of content appealed in Q1 2019 may have been actioned in Q4 2018.

You can learn more about how the appeals process works here.

New This Report: Data on Regulated Goods

We have longstanding policies against illicit drug and firearms sales. For years, we have used a range of approaches to enforce our policy such as: investigating profiles, Pages, Groups, hashtags and accounts associated with violating content we’ve already removed; blocking and filtering hundreds of terms associated with drug sales; and working with experts to stay updated on the latest tactics bad actors use to mask their activity, such as new street names for drugs.

In the summer of 2018, we began trying to use AI to identify content that violates our regulated goods policies. This investment has enabled us to take action on more content and, in the majority of cases, to do so before people need to report it to us. In Q1 2019, we took action on about 900,000 pieces of drug sale content, of which 83.3% we detected proactively. In the same period, we took action on about 670,000 pieces of firearm sale content, of which 69.9% we detected proactively.

By catching more violating posts proactively, this technology lets our team focus on spotting the next trends in how bad actors try to skirt our detection.

Our Commitment to Transparency

Over the last year, we’ve taken a number of steps to be more transparent in how we develop our policies and how we measure our efforts to enforce them. When it comes to our policies, we began sharing the meeting minutes from our bi-weekly meeting where we determine updates to our policies, and now provide a change log on the Community Standards website so that each month everyone can see where exactly we’ve made updates to our policies. Additionally, as part of our efforts to enable academic research, we are awarding grants for 19 research proposals across the world to study our content policies and how online content influences offline events.

Independent external review and input is an integral component to how we improve. In that spirit, we established the Data Transparency Advisory Group (DTAG) — comprised of international experts in measurement, statistics, criminology and governance — to provide an independent, public assessment of whether the metrics we share in the Community Standards Enforcement Report are meaningful and accurate. We provided the advisory group detailed and confidential information about our enforcement processes and measurement methodologies and this week they published their independent assessment. In its assessment, the advisory group noted that the Community Standards Enforcement Report is an important exercise in transparency and that they found our approach and methodology sound and reasonable. They also highlighted other areas where we could be more open in order to build more accountability and responsiveness to the people who use our platform. These important insights will help inform our future work.

We will continue to bring more transparency to our work and include more information about our efforts so people can see our progress and hold us accountable on where we need to do more.

You can read the full report here, and find the guide here.

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Press Call Transcript
Press Call Audio 
Data Snapshot
Hate Speech Proactive Detection
Appeals Chart
Regulated Goods Proactive Detection

By Laura McGorman and Alex Pompe, Data for Good

When planning public health campaigns or responding to disease outbreaks, health organizations need information on where intended beneficiaries live, as well as real-time insights from the field. However, in much of the world, information from the most recent census is often out of date and timely insights from remote communities are scarce.

Today, we are launching three types of maps that will help nonprofit organizations and universities working in public health get ahead of disease outbreaks and reach vulnerable communities more effectively. The maps we’re introducing — population density maps complete with demographic estimates, movement maps and network coverage maps — will help our health partners better understand where people live, how they are moving and whether they have connectivity. All of these maps, when combined with information from health systems, can improve the way organizations deliver supplies and respond to outbreaks.

“Epidemics pose a growing threat to lives and livelihoods,” said Vanessa Candeias, Head of Shaping the Future of Health and Healthcare at the World Economic Forum. “Mitigating their risk and impact requires every tool in the toolbox.”

Our initial partners for this effort, include: Direct Relief, FHI360, Harvard School of Public Health, the Institute for Health Metrics Evaluation at the University of Washington, International Medical Corps, the London School of Hygiene & Tropical Medicine, Malaria Atlas Project, the MRC Centre for Global Infectious Disease Analysis at Imperial College London, Northeastern University, Sabin Vaccine Institute, UNICEF, Wadhwani AI, the World Bank, and the World Economic Forum.

High-Resolution Population Density Maps Including Demographic Estimates

Two years ago, we began working with Columbia University to use satellite imagery and census data to build the world’s most detailed population maps to improve connectivity projects. After working with groups such as the American Red Cross and its Missing Maps project, we realized these maps could also have profound applications in public health.

Today, we’re releasing high-resolution maps that estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics, including the number of children under five, the number of women of reproductive age, as well as young and elderly populations, at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information. By combining these publicly and commercially available datasets with Facebook’s AI capabilities, we have created population maps that are 3X more detailed than any other source.

Health organizations are already using our AI-powered maps to help allocate resources to the areas where intended beneficiaries live, like in Malawi, where high-resolution maps helped the American Red Cross and its Missing Maps project identify areas with and without concentrations of people. This helped the Red Cross and other on-the-ground teams make more efficient decisions about where to deploy 3,000 health workers to deliver vaccination messaging during a measles campaign.

The above map shows the distribution of women of reproductive age (ages 18-49) spread across Tanzania, drawn from a combination of census data and satellite imagery.

Movement Maps

Public health officials often have challenges predicting where disease outbreaks, like malaria or cholera, will strike. However, cutting-edge research has found that pairing health system information with data on human mobility can yield valuable insights about diseases spread by human-to-human contact.

“Population movements are crucial for the spread of many infections — from influenza to measles, said Dr. Adam Kucharski, Assistant Professor in Mathematical Modeling at the London School of Hygiene & Tropical Medicine. “But historically it’s been very difficult for disease researchers to get information on these movement patterns.”

The above map shows movement between Greater London and surrounding areas by day. These data are useful to public health organizations looking at the spread of infectious diseases, such as the flu.

Our movement maps aggregate information from people who are using Facebook on their mobile phones with location services enabled, providing real-time snapshots into mobility patterns. Partner organizations can combine this data with information on specific cases of diseases to glean insights about where the next case of cholera or drug-resistant malaria is likely to occur. The improved insights from these forecasting models allow health systems to get ahead of an outbreak by prepositioning treatments where they’re likely to be needed most.

“This kind of data can be integrated into our epidemiological models to help us estimate how quickly a disease might spread, and where to put resources to contain it,” said Caroline Buckee, Associate Professor of Epidemiology at Harvard TH Chan School of Public Health.

Network Coverage Maps

Over the past decade, the increased availability of mobile health technologies has dramatically expanded access to health information. However, many countries still have areas with little or no access to the internet. Meanwhile, public health officials have limited resources to conduct house-to-house visits, as well as limited information on whether target populations can be reached online.

Because the majority of people use Facebook on mobile phones that rely on cellular networks, we can create real-time maps that show health organizations whether people can be reached with an online message in advance of activities like vaccination days or bed net distributions. This allows public health teams to prioritize where other forms of communication are required, reaching the maximum number of people at the lowest possible cost.

The above map shows the density of 3g network coverage availability across areas in the Democratic Republic of the Congo affected by the most recent Ebola outbreak.

We hope these disease prevention maps are a valuable resource for public health organizations and that this work helps improve the delivery of health services and the response to epidemics.

“This effort is also another welcome step forward in unlocking the full potential for public- and private-sector data to be combined to predict and forecast epidemics,” said Candeias of the World Economic Forum, said. In line with Sustainable Development Goal three on improving health for all, we look forward to our partners using these maps to improve health outcomes for communities around the world.

Our high-resolution population density maps can be found on our page on Humanitarian Data Exchange. New partners interested in using movement or network coverage data maps can reach out to This email address is being protected from spambots. You need JavaScript enabled to view it.. To learn more, visit our Data for Good website.

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