How voters turned out on Facebook

We just posted this on the Data Team Page, and I thought I would post it here as well.

When Facebook users in the United States logged into Facebook on Election Day this year, they were greeted by a message alerting them of voting activity on Facebook. Users could click a button to announce to their friends that they had already voted and see which of their friends had done the same.

These data about who on Facebook voted offer a new lens into the demographics and behaviors underpinning election returns.  There are a few caveats, (e.g., selection bias for those who are members of Facebook and who visit frequently, reporting bias, no verification, etc.), but we believe that looking at these data across a number of dimensions offers insight into what types of people decided to vote, when they went to the polls, and which factors may have influenced the election.

Voter turnout has been a central issue during this election cycle.  Would disillusioned voters stay home? Would there be an enthusiasm gap between Republicans and Democrats? By looking at those users who state their political affiliation on Facebook, we can see a significant discrepancy between the Democrats and Republicans: Dems were 3% less likely than Republicans to get out to the poll. In a number of House and Governor elections, this would have been enough to flip the vote.

We can also observe how people of different ages behaved. The figure above shows the proportion of users in each age bucket who said they voted as a fraction of the people who came to the site yesterday, broken down by political party. If you’re wondering if youth today are apathetic about voting, this graph is striking proof that of this fact. The height of voter turnout peaks at 65 years of age, while the lowest turnout occurs at 18 years of age. In fact, a 65 year old is almost 3 times as likely to vote as a younger counterpart.  This tracks results collected from traditional exit polls, which also show a 30% turnout gap between younger voters and older voters.  Furthermore, while Democrats were able to mobilize as many young voters as Republicans, Republicans were far more successful at mobilizing older voters.

Many of the seats in this year’s election were hotly contested.  But did voters respond by turning out to the polls? The map above shows voter turnout by state. There are a few general trends: lower rates of people in the South and the Northeast turned out. The two states with lowest voter turnout were New York and Utah, followed closely by Mississippi and Nebraska.

Another view into the state-level turnout is the relative percentage of voters who came out in each state. The map above shows the share of voters in each state: A blue state means Democrats were voting much more than Republicans, while red implies high Republican turnout relative to Democrats. States with an even number of Democrats and Republicans voting are grey.  Unsurprisingly, traditionally blue states on the Pacific coast and northeast are blue, while the South and mountain states are red.  Grey states partially reflect some of the most hotly disputed seats in battleground states, such as Nevada and Virginia.

On our election-day display we showed users which of their friends had voted; but how much effect could this have on voter turnout? Could people see their friends voting and go out to do the same? The above plot shows the probability that a person voted yesterday as a function of the fraction of their friends who had voted. As more and more of your friends vote, not surprisingly, you are more likely to vote. Unfortunately, we cannot tell whether this effect is because of social influence, or if voting practice is simply clustered at a local level, but the fact that voting behavior is shared between friends is quite clear.

Finally, we wanted to look into recent research which suggests that irrelevant events can have a large effect on voter turnout. It was expected that the winners of this year’s World Series would get a boost in voters, while the loser would see a decline. As we can see from the chart above, 6% fewer Rangers fans voted than Giants fans (go Giants!), but without any longitudinal data it is impossible to know if winning or playing in the world series had a causal effect on voter turnout.  Results, however, are suggestive.  It is worth noting that both Giants fans and Rangers fans turned out at rates significantly lower than others in those states (California and Texas). Having the last game of the World Series the night before the election probably means some people weren’t in the right mindset to go out and vote the very next morning.

This post was made possible by Jonathan Chang who crunched the numbers, Jason Bonta, Feng Qian, Nathan Schrenk and Doug Li who developed the election day tool and Adam Conner who brought our election day efforts together.

Introducing Facebook Fellowships

Today I’m happy to announce that Facebook will be offering fellowships to support graduate students in the 2010-2011 school year. The program will provide tuition, stipend and other perks to lucky students whose applications are chosen. Lots more details can be found on the Facebook Fellowship page.

The areas are quite broad, and reflect the range of problems we believe are important in shaping the future of social media and web engineering:

  • Internet Economics: auction theory and algorithmic game theory relevant to online advertising auctions.
  • Cloud Computing: storage, databases, and optimization for computing in a massively distributed environment.
  • Social Computing: models, algorithms and systems around social networks, social media, social search and collaborative environments.
  • Data Mining and Machine Learning: learning algorithms, feature generation, and evaluation methods to produce effective online and offline models of behavioral signals.
  • Systems: Hardware, operating system, runtime, and language support for fast, scalable, efficient data centers.
  • Information Retrieval: search algorithms, information extraction, question answering, cross-lingual retrieval and multimedia retrieval

If you or any Ph.D. students you know are interested in applying for the program, the deadlines are quite tight to make sure we can support students in the upcoming year. I’m really looking forward to seeing the applications. If you have any questions, please feel free to ask me or email the fellowship list at fellowships AT

How Diverse is Facebook?

In order to make Facebook as open and connected as possible for everyone, one of our goals is to understand how different populations of users join and use the service. With that objective in mind, the Facebook Data team recently sought to answer the question, “How diverse are the ethnic backgrounds of the people using Facebook?” This is a tough question to answer because, unlike information such as gender or age, Facebook does not ask users to share their ethnicity or race on their profiles. In order to answer it, we focused on a single country with a large and diverse population—the United States. Comparing people’s surnames on Facebook with data collected by the U.S. Census Bureau, we are able to estimate the racial breakdown of Facebook users over the history of the site.


We discovered that Facebook has always been diverse and that the diversity has increased significantly over the past year to the point where U.S. Facebook users nearly mirror the diversity of the overall population of the country. The graph above shows the proportion of the three largest minorities on Facebook over time as predicted by our model, while the dashed lines show the proportion of the Internet population for the same ethnicities.

In this report, we’ll discuss how we are able to measure diversity without user-supplied race or ethnicity. We’ll also explain how race and ethnicity have varied over the course of Facebook’s history and explore future research for understanding friendship diversity on the site.


The U.S. Census Bureau’s Genealogy Project publishes a data set containing the frequency of popular surnames along with a breakdown by race and ethnicity. These data are the key to our analysis, so we will spend some time describing them in some detail. An example of the raw data is shown below for the three most-frequent surnames in the census: Smith, Johnson and Williams. These data provide the rank in the population, the total count of people with the name, their proportion per 100,000 Americans, and the percent for various races: White, Black, Asian/Pacific Islander, American-Indian/Alaskan Native, two or more races and Hispanic respectively ((While there are many preferences for describing people’s race and ethnicity, we have chosen to use the terms used in the U.S. Census to be consistent with our data.)).

name rank count prop100k cum_100k white black api aian 2prace hisp
SMITH 1 2376206 880.85 880.85 73.35 22.22 0.4 0.85 1.63 1.56
JOHNSON 2 1857160 688.44 1569.3 61.55 33.8 0.42 0.91 1.82 1.5
WILLIAMS 3 1534042 568.66 2137.96 48.52 46.72 0.37 0.78 2.01 1.6

This data set allows us to predict what a person’s race is based solely on his or her surname. While these predictions will be often be wrong, in aggregate they will be correct. For example, suppose you select 10,000 people with the name Smith from the U.S. population at random. The data above suggest that 7,335 of them will be White, 2,222 will be Black and so on. Certain names will be more predictive of a certain race, while others will predict a wide array of ethnic backgrounds. The table below shows the top three names within the top 1,000 ordered by the percent in a given group. It shows that some ethnicities have distinctive surnames while others do not. For instance, 98.1% of individuals with the name Yoder are White while the most predictive name for American Indian / Alaskan Native individuals only has 4.4% in that group. For this reason, we will only look at White, Black, Asian/Pacific Islander and Hispanic predictions in our analysis.

Name Rank Count % in group
Yoder 707 44245 98.1%
Krueger 863 36694 97.1%
Mueller 467 64305 97.0%
African American
Washington 138 163036 89.9%
Jefferson 594 51361 75.2%
Booker 902 35101 65.6%
Asian / Pacific Islander
Zhang 963 33202 98.2%
Huang 697 44715 96.8%
Choi 872 57786 96.4%
American Indian / Alaskan Native
Lowery 752 41670 4.4%
Hunt 157 151986 3.9%
Sampson 844 37234 3.8%
Two or more races
Ali 876 36079 13.4%
Khan 665 46713 15.6%
Singh 396 72642 15.3%
Barajas 989 32147 96.0%
Orozco 690 45289 95.1%
Zavala 938 34068 95.1%

A simple technique for finding the distribution of ethnicities on Facebook is as follows: given the users who are on the site at a given time, sum the total users with each name in the Census Genealogy data. For each of these names, we estimate the total number of each ethnicity by multiplying by the numbers above. As in the previous example, if we have 10,000 Smiths on the site at one time, then we assume we have 7,335 White users, 2,222 Black users, and so on.

One potential source of error in this estimate comes from our assumption that users are selected at random from the U.S. population. What if Facebook is primarily White? Wouldn’t a majority of the Smiths be White then, breaking our assumption? In order to address this, we refine our estimates using a statistical technique known as mixture-modeling. We imagine that people come from a population with unknown racial/ethnic proportions. Individuals then get assigned names based on their race/ethnicity. Under this assumption, determining the ethnic makeup of Facebook becomes a problem of back-solving each individual’s ethnicity using only their revealed name. By allowing the Facebook population to be different from the Census population, and for each name to inform our interpretation of every other name, this technique allows us to more accurately estimate the expected number of Facebook users of a given race or ethnicity at any given time.

Finally, we adjust the estimates in our analyses with Internet adoption rates based on values from the National Telecommunications and Information Administration report on the Networked Nation. We use the percent of households with Internet access as a proxy for the addressable Internet population of each race or ethnicity.


Given the approach outlined in the methodology section, we obtain a picture of how the relative makeup of Facebook’s racial subpopulations within the United States. Because the Facebook population is changing over time, as is the ethnic diversity of addressable Internet users, we compare these groups over time. At each time step we recalibrate our model to account for the set of people on Facebook.

To illustrate this, the following plot shows how the model’s estimate of the distribution of the surname Lee has changed over time, tracking the change in Facebook’s population along with the change in our predictions of ethnicity. The dashed lines show the ethnic breakdown of people named Lee given by the Census Bureau tables described above. The disparity between the solid and dashed lines shows the possible bias when estimating race/ethnicity without the adjustment we describe in the previous section. For instance, the Census numbers would underestimate the number of Asian/Pacific Islanders on Facebook and overestimate the number of Black users on Facebook.


Looking at all users who have joined over the history of Facebook, we can examine the total population of that race on Facebook as predicted by our model at every point in time. These predictions are shown in the following chart. The chart conveys little about the diversity of Facebook since the growth of the site has affected all populations, and the U.S. population is predominantly White.


To look at the diversity of non-White users, the example shown at the top of this post shows our model prediction as a fraction of the Facebook population as well as the percent of the overall U.S. Internet population for each ethnicity. Here the solid lines show the Facebook percentage while the dashed lines show the U.S. population (in this case, we have chosen the U.S. population at the end of the time period). Because White users are a large majority, we have left them out of this plot.

Another approach to visualizing this data is to look at the relative saturation of each race. This is the fraction of users on Facebook compared to the fraction we would expect from the U.S. Internet population at that time. For instance, if Facebook had 100M users, and Asian Americans made up 4.4% of the U.S. Internet population, we would expect to find 4.4M Asian users on Facebook. If instead we observe 5M then the relative saturation would be roughly 114%.


The plot above shows Facebook saturation by ethnic and racial groups. Since 2005, Asian/Pacific Islanders have been much more likely to be on Facebook than Whites, and that has remained so. While Hispanics were once 40 percent as likely as Whites to be on the site, this number has been steadily climbing since early 2007 and currently is at 80 percent. This graph also shows that Black users are now about as likely to be on the site as White users.


In this post we have outlined an approach to determine the racial and ethnic breakdown of a population based solely on people’s surnames and data provided by the U.S. Census Bureau. We have found that while Facebook has always been diverse, this diversity has increased over time leading to a population that today looks very similar to the U.S. population.

Since completing this initial work, we have started using the first names of users to increase the precision of our predictions. While in this post we have only looked at the diversity of the population as a whole, we hope to use predictions of race and ethnicity for individuals, along with their friend connections, to understand how these populations of users are connected to each other. We are working to understand how diversity of interpersonal relationships is changing over time as more users join the site and find their friends.

The work in this post was a collaborative effort between the data scientists Lars Backstrom, Jonathan Chang, Cameron Marlow and Itamar Rosenn. This is a cross-post of the note on the Data Team Facebook Page.

B.J. Fogg at PARC today

B.J. Fogg, esteemed Stanford professor of persuasive computing, taught a class last semester about creating engaging Facebook applications. The students were, suffice to say, quite successful.

Mr. Fogg will be discussing the class today an open PARC forum titled, Facebook applications, mass persuasion, & world peace. The talk is 4-5pm at the George E. Pake Auditorium. It should be a pretty engaging discussion.