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Reading the Social Pages:Understanding and predicting the

demographics and behavior of Facebook users

Jonathan ChangFacebook Data Science

2010/04/16-17

mardi 27 avril 2010

350 million 30-day active users

0M

75M

150M

225M

300M

2004 2005 2006 2007 2008 2009

350M

mardi 27 avril 2010

Over 20 billion minutes spent every day

#2mardi 27 avril 2010

Jill Sparks commented on Frank Sparks’ Video.

Grandma’s Rose Garden.0:45 Recorded 2 days ago

Mark Zuckerberg commented on Dave Morin’s Photo.

May the iforce be with you.

Jane Richie wrote on Jim Morgan’s wall.Did you see the game tonight!?

Dan Jones wrote on Katie Lee’s wall. Great to see you last night. Catch you later.

Jim Morr commented on Jack Dean’s Photo.

Wow what a view. The GrandCanyon is just so amazing!

Garry Kindle commented on Sara Lima’s Photo.

Oooh Yum. Can I have someof that goodness? It’s making my mouth water.

14 of your friends are attending Family Picnic. It’s hosted by Jill Sparks. So far 18 people have been invited.

RSVP to this event

4 of your friends are attending after work drinks. It’s hosted by Jim Leman. So far 8 people have been invited.

RSVP to this event

Over 3.5 billion pieces of content shared every week

mardi 27 avril 2010

Over 2.5 billion photos uploaded each month

mardi 27 avril 2010

80,000 Facebook Connect implementations

mardi 27 avril 2010

Facebook Ads

• Amount: 100x million USD / year

• Coverage: > 10x billion of impressions / day

• Reach: >20% of 18 – 30 US male on Friday

mardi 27 avril 2010

What’s Different about FB Ads?

• Display Ads• Accurate demography• Social relationship• Long-term effects• Diverse formats

mardi 27 avril 2010

Ads Challenges

• Which ads should be shown to a user?

• Ad ranking and CTR estimation

• Ad scheduling and quality prediction

• User and contextual targeting• Social relationship between users

• How much should advertisers pay?

• Ad auction: GSP / VCG auctions

• Scaling: billions of ad impressions/day

mardi 27 avril 2010

Case Study: Keyword Insight Extraction for Advertisers (Juan & Jin)

• Do users clicking on an ad share a common set of keywords in their profiles and feeds?

• Issue: insufficient number of positive data

mardi 27 avril 2010

Keyword Extraction with Automatic Data Expansion

• Learn a click-through model using boosted trees with user / ad features.

• Propagate positive labels to top-ranked users.

• Assign negative labels to a random set of users.

• Rank the keywords by estimating the difference of information gain between pos / neg data.

mardi 27 avril 2010

Ad-Insight Extraction Results

Type KeywordTV sportscenterinterests girlsmovies anchormanmovies wedding crashersmovies Friday night lightsTV family guymovies happy gilmoreinterests footballinterests baseballinterests sports

Type KeywordTV family guyTV simpsonsMusic RockInterests girlsTV south parkInterests footballInterests carsTV 24Interests sportsMovies boondock saints

Football Van Wilder

mardi 27 avril 2010

Data Analysis

• Richest social data in the world• >1PB raw data managed in database today • >10k node Hadoop / MapReduce cluster• Process terabytes a day

• Applications• Ads, Feed, Search, Commerce• Growth, Site Integrity

mardi 27 avril 2010

What are users talking about (Lexicon)

• Extract popular words from user generated content (UGC)

• Slice by age, region

• Sentiment analysis

• Keyword association

Number of messages containing "Laid off"

Distribution of words related to "Vodka"

mardi 27 avril 2010

Lexicon: “Party” vs. “Hangover”

New Year’s Eve vs.New Year’s Day

Sat vs. Sun

mardi 27 avril 2010

2010-04-07 2010-04-08 2010-04-09 2010-04-10 2010-04-11 2010-04-12 2010-04-13 2010-04-14

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Taxes!

taxes

doing taxes

hate taxes

mardi 27 avril 2010

“Happiness” (Kramer et al.)

• “Happiness” is a broad concept

• Frequently measured using self-report

– Diener et al. (1985): Satisfaction with Life (SWL) is part of happiness

– Gallup-Healthways Well-Being Index

• Few behavioral indicators

– Gallup’s hybrid: “Did you smile a lot yesterday”

mardi 27 avril 2010

Measuring Happiness• Self-report is the “gold standard”

– Who is anyone to disagree with me if I say I’m (not) happy?

• Scaling self-report is not trivial

– Gallup telephones 1,000 people daily

mardi 27 avril 2010

The Pursuit of Happiness• An ideal measure of happiness

– Representatively samples population

– Based on naturalistic behavior

• Unobtrusive

– Is computational (no human raters)

– Covaries with other valid measures (e.g., non-naturalistic self-reports)

– Is efficient (must process millions of updates per day)

mardi 27 avril 2010

Data: Facebook Status Updates• More than 60 million status updates are posted

daily from many countries

• Updates used for self-expression

• Identified updates are then published to friends and the public

mardi 27 avril 2010

Word Counts• LIWC2007 Dictionaries: Word categories

representing psychological content (Pennebaker et al., 2007)

– E.g., pronouns, social words, emotion words (positive and negative)

– Unobtrusive; computer-coded

– Well validated in many corpora

• Diaries, essays, spoken word

mardi 27 avril 2010

LIWC: Example• “[Fred] hates passive-aggressive Facebook

updates, but loves irony.”

– “hate,” “aggress” are negative emotional terms

– “love” is a positive emotion term

– Update is 25% negative, 12.5% positive

mardi 27 avril 2010

Well-Being and Status Updates• Hypothesis:

– Happier people use more positive and/or fewer negative words

• Method:

– Recruited n=1,341 English speakers (via a Facebook ad) to complete Diener’s SWL scale

mardi 27 avril 2010

Study: Model

• Percent of words for an update were standardized relative to their country on the day of the update

• Positive and negative words standardized separately

• Result: “Happiness” per update

€

Hu =pu −µpd

σpd

−nu −µnd

σnd

mardi 27 avril 2010

Study: Results• Linear model predicted each update’s score from

user’s SWL

– 3 to 3,141 updates per user were analyzed, dating back to 2007

• SWL a significant predictor b=0.05, t(1339)=6.27, p < .001

– SWL measured on one day in 2009 predicts variability in one’s status updates over the past two years

mardi 27 avril 2010

Gross National Happiness

• Each country’s average % of words per day relative to other days

• Positivity and negativity separated

– Controls for differences in “positivity bias” or underreporting negativity

– Can be examined independently

€

GNHd =µpd −µp•

σp•

−µnd −µn•

σn•

mardi 27 avril 2010

Gross National Happiness

mardi 27 avril 2010

Ethnicities on Facebook

• Two competing hypotheses

• Suburbanization of Facebook / Ghettoization of Myspace.

• Facebook is representative of the US internet population.

• How do we answer this question?

• Facebook does not ask its users about their ethnicity.

• We infer their ethnicities of using only their names.

• Punchline

• First hypothesis was true, second hypothesis is true now.

mardi 27 avril 2010


• Our approach

• Leverage the machinery of topic modeling.

• Observed «words» are users’ first and last names.

• Treat hidden topic assignments as users’ ethnicities.

• Use census data as an informative prior.

• In this work we consider the four largest ethnic* populations.

mardi 27 avril 2010


R

R

N

α

θ

zn fn

ln βlr

βfr

η

ethnicity of user nzn

distribution of last names for ethnicity rβlr

distribution of first names for ethnicity rβfr

first name of user nfn

last name of user nln

ethnic distribution of populationθ

first name distribution priorη

ethnic distribution priorα

mardi 27 avril 2010


0%

20%

40%

60%

80%

100%

myspacelastnam

e.modelfullnam

e.modelcensus

.model

internet

group

Asian/Pacific−IslanderBlack

HispanicWhiteBecause there

is no Facebook ground truth, we test our method by scraping 10k users from Myspace.

mardi 27 avril 2010


Human judgment

0%

5%

10%

15%

20%

25%

30%

Asian/Pacific−Islander Black Hispanic White

factor(variable, levels = ordered.races)


HispanicWhite

Census only

mardi 27 avril 2010

Human judgment

0%

5%

10%

15%

20%

25%

30%




HispanicWhite


Topic model with only last names

mardi 27 avril 2010

Human judgment

0%

5%

10%

15%

20%

25%

30%




HispanicWhite


Topic model with first & last names

mardi 27 avril 2010

Ethnicities on FacebookPr

opor

tion

of M

inor

ities

on

Face

book

0%

2%

4%

6%

8%

10%

12%

jan−2006 jan−2007 jan−2008 jan−2009


HispanicWhite

mardi 27 avril 2010

Ethnicities on FacebookR

elat

ive S

atur

atio

n of

Eth

nici

ties

on F

aceb

ook

0%

50%

100%

150%

200%

jan−2006 jan−2007 jan−2008 jan−2009


HispanicWhite

mardi 27 avril 2010


Rank White Black Asian / Pacific Islander Hispanic

1 barb latoya rahul luis

2 conor latonya syed javier

3 peg deandre wei jose

4 deb lakeisha minh jorge

5 kurt tameka nguyen hector

6 colleen latrice tuan yesenia

7 meghan jermaine thanh mayra

8 meaghan lashonda sandeep julio

9 connor jamaal phuong alejandro

10 brendan lakisha yi cesar

Table 2: First names most associated with each ethnicity learned by

the proposed model.

an accurate distribution over ethnicities. In contrast, our pro-

posal, which takes this non-representativeness into account,

is able to better model the ethnic breakdown of Myspace

users.

We quantitatively measure the accuracy of each of these

models using a measure of distributional similarity, KL-

divergence, KL(q||p) =�

r q(r) logq(r)p(r) in the upper graph

of Figure 2. A lower KL-divergence means that the esti-

mated distribution is closer to the ground truth. As qual-

itatively observed in the previous paragraph, the internet

and census.model estimation techniques diverge from the

ground-truth, while the estimates of lastname.model and

fullname.model are more accurate. In KL-divergence, full-

name.model performs better than lastname.model. Because

our data set is small, the amount of information that can be

learned about each first name is small; we hypothesize that

for larger data sets where more can be learned about each

first name, the boost in accuracy of fullname.model over

lastname.model will be larger.

In Figure 3, we visualize the ability of the proposed model

to predict the accuracy of individual ethnicities in addition

to the ethnic breakdown of the aggregate population. Each

column represents a ground truth ethnicity; the stacks indi-

cate the number of people predicted by the model to be of

each ethnicity for that ground truth ethnicity. Ideally, the first

bar would be entirely red, the second entirely green, and so

on. Using the census model described in Section 2.1 will

underestimate Asian/Pacific-Islanders, Blacks, and Hispan-

ics. Using our model with just last names improves upon

this estimate, increasing the number of people correctly iden-

tified in each of these categories. Using both first and last

names further improves estimates, largely by making better

distinctions between White and Black.

The model is able to learn first names associated with each

ethnicity and leverage this information to improve its esti-

mates of individuals’ ethnicity. To illustrate this, we apply

the model to the larger user base of U.S. Facebook users (de-

scribed in the following section) to generate Table 2, which

shows the first names most indicative of each ethnicity, i.e.,

those names with the highest smoothed posterior probability

for each ethnicity.

4. Application to Facebook users

In this section we apply the approach described in Section 2.2

to the set of U.S. Facebook users. Using our proposed model,

Ethn

ic B

reak

dow

n of

Fac

eboo

k U

sers

Nam

ed L

EE

0%

20%

40%

60%

80%

100%

jan−2006 jan−2007 jan−2008 jan−2009

variable


HispanicWhite

Figure 4: Ethnic breakdown of U.S. Facebook users with the sur-

name LEE over time (solid lines). Dashed lines show the proportion

of each ethnicity in the census.

Prop

ortio

n of

Min

oriti

es o

n Fa

cebo

ok

0%

2%

4%

6%

8%

10%

12%

jan−2006 jan−2007 jan−2008 jan−2009


HispanicWhite

Figure 5: Ethnic breakdown of U.S. Facebook users over time (solid

lines). Dashed lines show the proportion of each ethnicity in the

addressable Internet population. Although Asian/Pacific-Islanders

are still overrepresented, other minorities have nearly reached parity

with the addressable Internet population.

we report on how the diversity of Facebook has changed over

time, how members of different ethnicities interact, and the

demographic characteristics of each ethnicity.

Diversity over time The technique of Section 2.2 allows

us to obtain a picture of 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.

To illustrate how our model changes along with the chang-

ing population of Facebook users, Figure 4 shows the model’s

changing estimate of the distribution for the surname LEE.

The dashed lines show the ethnic breakdown of people named

LEE given by the Census Bureau tables. The disparity be-

tween the solid and dashed lines shows the possible bias

when estimating ethnicity without the adjustments made by

our model. For instance, the Census numbers would under-

estimate the number of Asian/Pacific Islanders on Facebook

and overestimate the number of Black users on Facebook.

Figure 5 shows our model’s prediction of the ethnic break-

down of the U.S. Facebook population over time (solid lines).

mardi 27 avril 2010


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Asian/Pacific−Islander

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Hispanic

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−120 −110 −100 −90 −80 −70

mardi 27 avril 2010


Value relative to overall mean

Not specified

Very liberal

Liberal

Moderate

Conservative

Very Conservative

Apathetic

Libertarian

Other

0.4 0.6 0.8 1.0 1.2 1.4

X2


HispanicWhite

mardi 27 avril 2010



Black

Hispanic

White


Friendingmardi 27 avril 2010


Black

Hispanic

White



Relationshipsmardi 27 avril 2010

initiator

recipient


Black

Hispanic

White


48%49%50%51%52%


Initiationmardi 27 avril 2010


Number of friends

RM

SE

10−3.5

10−3

10−2.5

10−2

10−1.5

10−1

10−0.5 ●

100 100.5 101 101.5 102 102.5 103 103.5

Homophily suggests that your ethnicity can be predicted by your friends’ ethnicity.

mardi 27 avril 2010

Inferring politics Politics are an important demographic

characteristic.

Self-reported is political affiliation is sparse and not necessarily consistent across users.

How can we infer meaningful political positions for a large proportion of our user base?

mardi 27 avril 2010

Inferring politicsAn Approach:

We use an ideal point model, mapping each user’s political position to a 1-dimensional latent space.

To propagate information, we exploit the page fanning network.

• Users can fan pages, expressing a positive disposition towards the product, person, organization, etc.

• This induces a bipartite network.

mardi 27 avril 2010

Inferring politics

U P

μpπu σp

F

fu,p

Associate each user u with a political position πu.

Associate each page p with parameters μp, σp describing the political position of its fans.

Generate fan edges fu,p with probability N(πu ; μp, σp).

mardi 27 avril 2010

Inferring politics20

25

30

35

40

45

50

baseline

-130 -120 -110 -100 -90 -80 -70 -60

20

25

30

35

40

45

50

imputed

mardi 27 avril 2010

Inferring politicsMost liberal cities Most conservative cities

New York Birmingham

San Francisco Fort Worth

Los Angeles Tulsa

Seattle Jacksonville

Boston Nashville

Miami Memphis

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Geography of US FB Population(Backstrom et al.)

We have two ways to determine location

▪ From IP Address▪ Lots of inaccurate, bad data

▪ From user provided address▪ Also lots of inaccurate, bad data▪ But, more precise when correct

Using user provided data, we can map FB usage

▪ More interesting when compared to US Population▪ Surprisingly midwest is highest

FB Density

FB Density, normalized by Popluation

mardi 27 avril 2010

DemographicsConsider each 0.01x0.01 mile region in the US▪ How many FB users are there in each region?

mardi 27 avril 2010

Demographics▪ Distribution shows two distinct behaviors on log-log plot

▪ Rural Region, slow falloff; Urban Region, fast falloff

mardi 27 avril 2010

Population DensityWe divide the US into three regions▪ Low, medium and high density – 1/3 of users in each

For each person, we count people in annulus of width 0.1 mile and radius r

0.1

mardi 27 avril 2010

Population Density▪ High density regions have more people nearby▪ Curves converge at 50 miles▪ High density curve goes down as we transition urban to rural

mardi 27 avril 2010

Distance to friendsWhat is the probability that you know someone x miles away?▪ Count the number you know x miles away▪ Count the total number of people x miles away▪ Divide and aggregate over all users▪ Tail of curve is roughly x-1

mardi 27 avril 2010

Distance to friendsBreaking down by density▪ Higher density -> lower probabilities at lower distances (larger denominator)

▪ At ~100 miles all curves converge▪ Beyond 100 miles, urban users have higher probability (more cosmopolitan?)

mardi 27 avril 2010

An ApplicationUsing these data, can we predict where a person lives, based on their

friends’ locations?▪ Data suggests probabilistic model where p(friend(u,v)) = (dist(u,v) + α)-β

▪ Given the location of a user’s friends find maximum likelihood location.

Model does better than IP at placing users within a few miles

Frac

tio

n w

ith

in x

Mil

es

Milesmardi 27 avril 2010

An ApplicationPerformance increases with number of located friends.

Located Friends

Med

ian

Err

or

mardi 27 avril 2010

ConclusionsFacebook has a massive and rich social data set.

These data are useful in understanding the demographics of our user base:

▪ Happiness

▪ Ethnicity

▪ Political Bent

▪ Geographic Distribution

Our results have suggestive implications for questions in social science, political science, etc.

There’s still a lot of untapped potential in these data!

mardi 27 avril 2010

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