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“I Need to Try This!”: A Statistical Overview of Pinterest
Eric Gilbert, Saeideh Bakhshi
School of Interactive Computing
Georgia Institute of Technology
[gilbert, sbakhshi]@cc.gatech.edu
ABSTRACT
Over the past decade, social network sites have become ubiquitous places for people to maintain relationships, as well
as loci of intense research interest. Recently, a new site has
exploded into prominence: Pinterest became the fastest social
network to reach 10M users, growing 4000% in 2011 alone.
While many Pinterest articles have appeared in the popular
press, there has been little scholarly work so far. In this paper,
we use a quantitative approach to study three research questions about the site. What drives activity on Pinterest? What
role does gender play in the site’s social connections? And
finally, what distinguishes Pinterest from existing networks,
in particular Twitter? In short, we find that being female
means more repins, but fewer followers, and that four verbs
set Pinterest apart from Twitter: use, look, want and need.
This work serves as an early snapshot of Pinterest that later
work can leverage.
Author Keywords
social network sites; cmc; pinterest; twitter
ACM Classification Keywords
H.5.3 [Group and Organization Interfaces]: Asynchronous
interaction - Web-based interaction;
INTRODUCTION
Over the past decade, online social networks have become
ubiquitous places for people to maintain and create social
relationships, exchange messages with friends and family,
share jokes and news, ask and answer questions, etc. Facebook is the largest online social network, with a staggering 1
billion registered users from all around the world. In addition,
Twitter, Google+, and LinkedIn all have over 100 million
users, and the two most popular Chinese networks—Weibo
and Renren—each have over 300 million users.
Not surprisingly, social network sites have become a locus
of intense interest for researchers from a wide range of disciplines. Using a variety of methods, researchers study issues
such as whether social media traces can predict social network
tie strength [16], and build and maintain social capital [7, 14].
Shuo Chang, Loren Terveen
Dept. of Computer Science & Engineering
University of Minnesota
[schang, terveen]@cs.umn.edu
They have also developed new techniques, including new
approaches for recommending content [12] and friends [11].
In the past year, a new social network site has exploded
into prominence: Pinterest became the fastest social network
to reach 10 million users, growing 4000% in 2011 alone
[26]. Pinterest revolves around the metaphor of a “pin board:”
users “pin” photos they find on the web and organize them
into topical collections, such as hobbies, sports, fashion, etc.
Pinterest users—called pinners—can follow one another and
also “re-pin”, “like”, and comment on other pins.
In Pinterest’s own words, the site’s “goal is to connect everyone in the world through the ’things’ they find interesting. We
think that a favorite book, toy, or recipe can reveal a common
link between two people.” 1 . This focus on “things” has made
Pinterest of great interest to online retailers and marketers:
for example, a recent market survey showed that a higher
proportion of Pinterest users click through to e-commerce
sites, and when they go there, they spent significantly more
money than people who come from sites like Facebook or
Twitter [24]. Another frequently discussed property of Pinterest is its demographics. In the United States, about 80% of
its users are women; interestingly, however, fewer than 45%
of Pinterest users in the United Kingdom are female [2].
While there is a large amount of writing about Pinterest in
the popular, trade, and marketing press, there has been little
scholarly work so far. As researchers, we want to systematically investigate impressions of Pinterest that come from
anecdotal observations and market surveys. We defined three
questions to guide our research:
R1-Activity: What drives user activity on Pinterest: what
about a pin—and its pinner—“grabs the attention” of
other users? For example, what role does gender play?
Do pins from women receive more, less, or equal attention than those from men?
R2-Connection: What is the structure of social connection
on Pinterest? For example, are women or men more
connected?
R3-Comparison: How does behavior on Pinterest compare
to behavior on other social network sites? Letting Twitter
be our point of reference, do Pinterest and Twitter users
systematically differ in what they talk about?
1
http://pinterest.com/about
We used a quantitative approach to study these questions,
performing a statistical analysis of data obtained from a web
crawl of Pinterest. In addition to our specific findings, our
results serve as an early snapshot of Pinterest that subsequent
work will be able to use as a baseline.
In the remainder of the paper, we survey related work, describe the data we analyzed and how we obtained it, explain
our statistical methods, present our results, and discuss their
implications. Finally, we close with a brief summary.
RELATED WORK
There is a vast research literature on online communities and
social networks. Researchers have applied a wide range of
methods to study diverse types of research questions. For
example, Ellison and Lampe have done a number of survey
studies of Facebook users, showing that people use Facebook
to maintain existing offline connections [20], identifying motivations for using Facebook, and showing that using Facebook
builds social capital [14]. Other research has analyzed traces
of interaction in social sites, identifying interaction patterns
that: relate to social capital [7]; predict strength of social
ties [16]; identify categories of status updates [22]; and characterize the practice of passing along messages from others
(“retweeting”) [4].
The prior research most relevant to our research questions
and methods consists of quantitative studies of what makes
content interesting to members of a social network and the
role of gender in online social interaction.
What Makes Content Interesting
Much research attention has gone into investigating what
makes content in an online community interesting to its members, that is, what they pay attention to. Two primary contexts
in which these investigations have been done are discussion
forums and Twitter.
For discussion forums, this problem takes the form of understanding and predicting what types of posts are most likely
to receive replies. In a series of studies conducted on Usenet
newsgroups, researchers from CMU investigated properties
of the post, poster, and the newsgroup itself that influenced
likelihood of reply. Early findings included: writing explicit
requests, including personal “testimonials” relating one’s
connection to the group, and staying on-topic increased the
probability of receiving a reply; and newcomers to a group
were less likely to receive a reply than more established members [1]. There were several follow-ups to this work. Burke
et al. [5] studied the role of self-disclosing introductions,
signaled by first-person pronouns, mention’s of the poster’s
age, and an acknowledgement that this is the poster’s first
post. These factors were found to increase reply probability.
Burke and Kraut [6] also studied the effect of the politeness
of a post, with the interesting finding that politeness leads
to more replies in certain types of groups, while in others
rudeness actually leads to more replies.
Other researchers have studied factors that predict not just
getting a reply, but the quality of the reply. For example,
Harper [17] studied Q&A sites and found that the type of
a question (e.g., for personal advice or technical assistance)
and the question’s topic influenced reply quality.
Turning to Twitter, researchers have used retweeting—reposting someone else’s tweet—as a measure of community
interest, and have investigated what features of tweets and
users predict retweeting. Suh [27] found that the presence of
URLs and hashtags in tweets predicted more retweeting, as
did a richer connection with the community, both in followers
(a larger audience) and followees (perhaps indicating access
to more diverse information). A more general perspective is
that of influence, or flow of information through a network.
For example, Cha [10] computed three different measures
of influence in Twitter—in-degree in the Twitter social network, retweets of content, and mentions in tweets—and then
examined the extent to which the different measures were
correlated. Lerman and Ghosh [21] studied the flow of information in both the social news site Digg and in Twitter
(where they took retweets as the mechanism of influence).
Our work explores some of the same issues as prior work, but
on a new social network site. In particular, we investigated
which properties of a pin and its pinner lead the Pinterest
community to pay it more attention.
Gender
How gender is expressed in and influences online social interaction is a topic of great research interest. Herring wrote an
overview of the literature prior to the dawn of online social
networks [18]. A common theme was to study the extent
to which gender played a role in online interaction similar
to what is seen offline, and to examine whether factors like
moderation and anonymity changed its role.
This research was done in an era when online interaction
was—or at least was perceived to be—dominated by men
and male norms of communication. This began to change
as more online spaces were designed by and for women.
And the rise of social network sites has changed this picture
dramatically: for example, recent data [28] shows that women
form a majority of Facebook and Twitter users, as well as
dominating Pinterest; however, men are the majority of users
on Google+ and LinkedIn.
For our purposes, studies that use quantitative methods to analyze behavioral data from social networks are most relevant.
Caverlee and Webb analyzed data from MySpace, finding
that women were twice as likely as men to make their profiles
private, and that the most popular terms used by men and
women in their profiles were starkly different [9]. Thelwall
also analyzed MySpace data, with similar findings to Caverlee & Webb; additionally, he found that women had more
friends than men, and that both men and women had more
female than male friends [29]. Cunha found that male and
female Twitter users differed in what hashtags they used for
common topics [13].
Spurred by reports in the popular media, several recent studies
used quantitative methods to investigate gender disparities in
Wikipedia. In particular, the findings of Lam [19] include:
women editors focused on different topics than men, topics
of most interest to women had lower quality coverage, and
that new female editors are significantly more likely to have
their edits reverted than are new male editors.
location: a free-text description of a pinner’s location, via
either Pinterest or Facebook; if they specified it in both
places, we used the Pinterest location;
gender: a pinner’s self-reported gender via their Facebook
profile page;
tweet text: via their linked Twitter handle, the text of all
tweets written by this pinner in the last year.
seed dataset: 1K popular pins
GENERATE RANDOM PIN
2.9M pins. from each: repins, likes, comments, text, pinner
989K pinners. from each: followers, follows, boards, pin, location
facebook gender + location
2.6K pinners who tweet
text from 217,174 pins
text from 737,419 tweets
Figure 1. A flowchart of the steps taken in this paper to prepare data
for modeling. We cover these steps in detail in the body of the paper, but
include this figure for overview and reference. Yellow signifies data we
use to answer research questions R1 and R2, while blue signifies data
that answer R3.
In our research on Pinterest, we examine some similar issues
to prior work, notably whether the gender of a pinner predicts
the amount of attention the community gives to the user’s
pins, as well as the existence of gender-based differences in
number of social connections.
DATA AND METHODS
We took a quantitative approach to investigate our research
questions. We describe the data we collected and the statistical methods we used2 . We summarize the limits of our approach at the end of the paper. Figure 1 presents an overview
of the data presented in this paper.
Data
The data we needed from Pinterest comprised pins and pinners. For pins, we were interested in data that signaled interest
from the Pinterest community:
repins: how many times this pin was repinned;
likes: how many other users liked this pin;
comments: textual comments included by the pin’s pinner;
text: the pin’s description and comments by others.
For pinners, we were interested in data indicating their activity on Pinterest and relevant personal characteristics:
followers: how many pinners follow this pinner;
follows: how many users this pinner follows;
boards: how many boards this pinner has created;
pins: how many pins this pinner has created;
Pinterest suggests to pinners that they include their Facebook
and Twitter handles in their profiles. This let us obtain three
additional data elements (where available):
Obtaining the data
While our goal was to obtain a random sample of pins and
pinners, there is no publicly available means to do so. Instead,
we developed a web crawler to approximate a random sample.
Pinterest does not have a page that lists all pins or pinners;
however, it does provide a list of “popular” pins3 . In June
2012, we ran our web crawler to collect popular pins using
884 machines allocated via PlanetLab4 . This gave us a “seed
set” of approximately 1,000 pins.
We next wanted to expand our sample beyond “popular pins.”
We experimented with a variety of strategies, such as snowball sampling (which brings with it certain complications [3]).
We eventually discovered, however, that we could reverseengineer a pin’s URL identifier structure to generate an apparently random view of the universe of Pinterest pins. We
observed that adding small integers to the identifier in a pin’s
URL would find new pins created slightly later. Via trial and
error, we discovered that the trailing five digits of the identifier encoded a pin’s creation time. Therefore, we generated
new pins by adding random integers in the range [1, 10,000]
to our seed set of popular pins. We found no correlations
among these pins in terms of boards (i.e., categories), pinners,
popularity, location, etc. Obviously, we cannot make any
absolute claims about the randomness of this sample. However, by inspection it looked like a “public timeline” view of
Pinterest, and seemed preferable to a snowball sample crawl.
Using this method, we collected a total of 2.9M pins and their
associated data as noted above. We also collected the pinners
for these pins, ending up with a set of 989,355 pinners.
For every pinner who specified their Facebook ID, we checked
whether they had a public Facebook profile. If so, we obtained
their self-reported gender and location, if provided, as noted
earlier. In addition, from a pinner who specified their public
Twitter handle, we selected a random set of 2,616 users. We
then obtained all tweets from these pinners from the last year,
as well as all as the text from these pinners’ pins. In total, we
collected datasets of 217,174 pins and 737,491 tweets.
Preparing the data for analysis
Once we obtained the basic data described above, we had to
do some additional work to prepare it for analysis.
Location. The location data we obtained from Pinterest and
Facebook typically were not clean, normalized geo-location
data. Therefore, we used the Google Maps API5 to conform
user-entered location data to the level of a country. For the
purpose of our models, we operationalize this as 45 binary
3
http://pinterest.com/popular
https://www.planet-lab.org
5
https://developers.google.com/maps
4
2
Our dataset: https://github.com/compsocial
Variable
pin’s repins
pin’s likes
pin’s comments
pinner’s followers
pinner’s follows
pinner’s boards
pinner’s pins
med
mean
max
0
0
0
86
86
16
1K
0.96
0.213
0.012
288.5
125.1
21.2
1.7K
5K
980
46
195K
29K
390
95K
distribution
(categorical)
pinner’s gender
country
52.9K female
49.8K USA
3.1K Brazil
2.5K Spain
6.9K elsewhere
13.3K male
27.7K Great Britain
2.9K France
1.1K Netherlands
Table 1. Basic statistical descriptions of the variables collected in this
paper. All quantitative variables have zero as their minimum. All distribution sparklines begin at zero and end at their variable’s maximum
value, on log-scale. The distributions themselves were induced with
Gaussian kernel density estimates, a smoothing procedure.
country variables, such as from united states and from china.
We experimented with a more refined latitude and longitude
operationalization, but this led (as might be expected) to too
many confounds in the models.
Text. As noted above, we collected text for over 200K pins—
their descriptions and any comments from other users—and
over 700K tweets. This text serves as the basis for our investigation of whether users behave differently on Pinterest than
on other social networking sites: specifically, do they talk
about different things than they do on Twitter? We think this
gives a richer characterization of behavior than simply modeling count variables such as likes and repins. Observe that
this is a within-subjects sample, so we can more confidently
attribute differences in language use to differences between
Pinterest and Twitter, rather than to differences between the
user populations of the sites.
We transformed all the pins and tweet text into a sparse matrix
representation. The columns of the matrix represent all the
words used in the entire corpus, and a “1” in a column indicates that that column’s word appeared in the corresponding
pin or tweet. We explain in detail below how our statistical
methods use this matrix.
Statistical Methods
We used two statistical techniques to examine activity on
Pinterest. For our first research question, we used negative
binomial regression to model Pinterest activity as a function
of the predictive variables listed above. We use negative binomial regression because our dependent variable is a count. For
our second research question, we also used negative binomial
regression. For our third question, we used penalized logistic regression (PLR) [15] to study the differences between
Twitter and Pinterest text. PLR allows us to determine the
relative membership of each phrase in Twitter and Pinterest.
Repins predictor
β
std. err
z
p
intercept
pin’s likes
pin’s comments
pinner is female
pinner’s followers
pinner’s follows
pinner’s boards
pinner’s pins
from united states
from great britain
from canada
0.423
0.527
0.22
0.0801
0.000733
0.0000398
-0.000226
-0.0000124
0.104
0.0773
0.102
0.007
0.003
0.009
0.005
< 10−5
< 10−5
< 10−4
< 10−6
0.005
0.006
0.0258
60.3
190
25.1
15
178
9.45
-9.1
-70.7
20.6
12.3
3.95
< 10
< 10−15
< 10−15
< 10−15
< 10−15
< 10−15
< 10−15
< 10−15
< 10−15
< 10−15
0.00008
null dev.
res. dev.
χ2
p
864K
476K
388K
< 10−15
Summary
−15
Table 2. The results of a negative binomial regression with number of
repins as the dependent variable. We include only a selection of the
geo-location variables in this model, as only these three countries corresponded to more than 1,000 data points. In the model summary, Dev.
refers to deviance, a measure of the goodness of fit similar to the betterknown R2 . The β coefficients here are not standardized; they remain
on their original scales.
Implemented in the R package glmnet6 , PLR predicts a
binary response variable while guarding against collinearity
among predictors, something not done by traditional logistic
regression. This is important in this context since language
often exhibits high degrees of collinearity: i.e., the word
transportation will follow the word public much more often
than mouse. The implementation of PLR we employ in this
paper handles correlated predictors using the lasso technique,
shifting most of a group of correlated predictors into the mass
of the most predictive feature. Consequently, the vast majority get left out of the model, with PLR assigning them zero
coefficients. The implementation also handles sparsity, which
is important for natural language processing since a message
contains only a small percentage of the globally observed
phrases. While neither negative binomial regression or PLR
likely offers the most powerful purely predictive model (i.e,
SVMs would likely provide better predictive accuracy), these
statistical approaches permit close inspection of the importance of the predictive variables. This is important since we
care most about illustrating the forces driving Pinterest use.
RESULTS
Descriptive Statistics. Table 1 reports basic statistical descriptions of the variables (predictors) collected in this paper.
Table 1 does not report minimums, since they are zero for
all the quantitative variables. As is typical for social datasets,
the means and medians of these variables often differ considerably. The last column of Table 1 shows distributions
that illustrate the shape of the variable. We induced these
distributions by computing Gaussian kernel density estimates
over the log-transformed variable. Gaussian kernel density
estimates smooth datasets in an attempt to expose a more
6
http://cran.r-project.org/web/packages/glmnet
Figure 2. A heatmap of the distribution of Pinterest users by gender across the world. Females are represented as orange and males as gray on the
map. The U.S. and Europe (particularly the U.K.) dominate Pinterest membership.
continuous, realistic picture of the underlying distribution [8].
We used log-transformation because otherwise a handful of
data points would dominate the charts. We also summarize
the distributions of the categorical variables, ordering country
membership in descending order. For example, roughly 53%
of the pinners in our sample come from the United States,
while 29.4% live in Great Britain.
Figure 2 plots the location of the pinners in our sample on
a map of the world, using color coding to distinguish men
and women. This illustrates both where Pinterest users come
from, and how gender of Pinterest users differs by region: for
example, we see a higher concentration of men (represented
by gray dots) in Europe than in the United States.
We next present the results of the analyses we did to investigate our research questions.
Followers predictor
β
std. err
z
p
intercept
pinner is female
pinner’s follows
pinner’s boards
pinner’s pins
from united states
from great britain
from canada
5.89
-1.77
0.00294
-0.00391
0.000339
0.0917
0.19
-0.891
0.0103
0.007
< 10−5
< 10−4
< 10−6
0.00819
0.0101
0.0665
571.72
-234
422
-47.8
456
11.19
18.84
-13.39
< 10
< 10−15
< 10−15
< 10−15
< 10−15
< 10−15
< 10−15
< 10−15
Summary
null dev.
res. dev.
χ2
p
1.3M
789K
523K
< 10−15
−15
Table 3. The results of a negative binomial regression with number of
followers as the dependent variable. Again, we include only a selection
of the geo-location variables in this model, as only these three countries
corresponded to more than 1,000 data points. The β coefficients here
are not standardized; they remain on their original scales.
R1-Activity: What drives activity on Pinterest?
We used standard techniques to measure the effectiveness
of our models that investigated what drove Pinterest activity; specifically, we compared them to intercept-only (null)
models and examined the reduction in deviance. Taking pin’s
repins as the dependent variable and using all others as predictor variables in a negative binomial regression provides
considerable explanatory power, with an improvement in
deviance of 864K - 476K = 388K. Deviance is related to a
model’s log-likelihood, and is an analog of the R2 statistic
for linear models A difference in deviances approximately
follows an χ2 distribution, so we can test for the overall likelihood of this model explaining our data, χ2 (10, N=588K7 )
= 864K - 476K = 388K, p < 10−15 . Table 2 summarizes the
predictors and overall fit of the pin’s repins model.
Table 2 only presents three country-level variables because
most countries are only sparsely represented and this caused
problems for fitting a negative binomial model. All the variables possess non-random coefficients, with pin’s likes perhaps unsurprisingly driving pin’s repins the most, β = 0.527,
p < 10−15 (even on its original scale). Note that in this paper’s
models, we have opted to not standardize β weights, so they
remain on their original scales. This is one reason they vary
by orders of magnitude, for instance.
R2-Connection: What structures connections?
Table 3 presents the results of a negative binomial regression,
with follower count as the dependent variable. The overall
model explains a considerable amount of overall deviance,
χ2 (7, N=672K) = 1.3M - 789K = 523K, p < 10−15 . Perhaps
surprisingly, given the large proportion of women on Pinterest
and the gendered rhetoric often used to describe the site, being
female suggests fewer followers β = -1.77, p < 10−15 .
7
N=588K because only 588K of the 2.9M pins we obtained from
Pinterest had gender and geo-location information.
Pinterest words
DIY
cup
recipe
idea
hair
use
pretty
baby
love
great
look
before
way
one
need
little
girl
β
3.003
2.427
1.745
1.71
1.344
0.969
0.721
0.641
0.537
0.429
0.31
0.2
0.168
0.118
0.0916
0.0685
0
Pinterest words
[unicode heart]
cute
dress
color
Love
Great
old
made
book
photo
awesome
want
fun
over
kid
home
none
β
2.776
1.746
1.718
1.52
1.072
0.919
0.671
0.624
0.506
0.363
0.263
0.173
0.156
0.112
0.085
0.0483
0
Table 4. The 34 phrases predicting a post belongs to Pinterest, not Twitter. The table flows left to right, then top to bottom. All phrases are
significant at the 0.001 level.
Figure 3, on the other hand, shows follower count distributions by gender. We provide this figure because something
interesting is happening: while the median male follower
count is lower than the female median follower count (67 vs.
86, respectively), the mean male follower count is substantially higher than the mean female follower count (1,063 vs.
270, respectively). You can see this fact visually in Figure
3 by the fatter tail in the gray distribution. We revisit what
might be driving this difference later in the Discussion.
R3-Comparison: How do Pinterest and Twitter compare?
Tables 4 and 5 present the results of our PLR model, showing words most effective for distinguishing Pinterest from
Twitter discourse. As mentioned earlier, our implementation
of PLR will assign most variables zero coefficients in order
to discover a parsimonious model. While we have separated
our results into two tables, all the coefficients derive from
the same model, with dependent variable post from pinterest.
The overall model explains a significant, if modest, amount
of the variance distinguishing Pinterest from Twitter, χ2 (98,
N=955K) = 1.03M - 816K = 219K, p < 10−15 . All of the
words in Tables 4 and 5 are significant at the 0.001 level, the
default cutoff for inclusion in a glmnet model. This model,
based solely on words contained in tweets and pins, accounts
for approximately 21% of the deviance in the dependent
variable. A model that incorporates other features important
to Pinterest and Twitter (e.g., image features) presumably
would explain more.
Figure 4 provides a deeper view into the structure around
the predictive words in Tables 4 and 5. It shows Word Tree
visualizations [32] (created using the Many Eyes site [30])
of searches for “want to” and “need to” in only the text from
Pinterest. For example, Figure 4 shows that the phrases “want
to do” and “want to make” appear many times across multiple
Twitter words
"
Thanks
watching
today
la
going
friend
feel
:-D
still
here
think
ca
go
wait
night
come
really
thing
time
day
next
always
Oh
something
back
better
blog
more
take
year
down
β
-4.946
-2.859
-2.736
-2.213
-1.694
-1.273
-1.006
-0.969
-0.959
-0.948
-0.92
-0.903
-0.885
-0.747
-0.703
-0.683
-0.653
-0.603
-0.597
-0.545
-0.512
-0.5
-0.473
-0.408
-0.371
-0.311
-0.31
-0.241
-0.169
-0.105
-0.053
-0.012
Twitter words
que
thanks
tonight
Thank
Happy
lol
see
new
last
getting
haha
people
sure
now
know
week
Photo
right
well
much
work
New
oh
good
find
first
even
Good
never
Day
very
life
β
-3.7
-2.849
-2.403
-2.134
-1.46
-1.193
-0.989
-0.96
-0.948
-0.929
-0.906
-0.9
-0.88
-0.717
-0.689
-0.681
-0.614
-0.602
-0.56
-0.54
-0.508
-0.482
-0.441
-0.404
-0.317
-0.31
-0.31
-0.189
-0.155
-0.089
-0.017
-0.011
Table 5. The 64 phrases that predict a post belongs to Twitter and not
Pinterest. The table flows left to right, then top to bottom. All phrases
are significant at the 0.001 level.
pins. We think it offers an intriguing, deeper look into the
structure of the text behind our statistical techniques.
DISCUSSION
We have presented a number of statistical models investigating behavior on Pinterest. We now reflect on these findings
as framed by our research questions.
R1-Activity: What drives activity on Pinterest?
Table 1 shows a few surprising and telling features of Pinterest as compared to other online social networks. First,
while repins, likes and comments are all relatively rare, repins happen considerably more often than either likes or
comments. This shows that reposting content from others—
here, repinning—is a first-class activity, much like retweeting
on Twitter and re-blogging on Tumblr. Furthermore, we see
that on average pinners have 86 other pinners following them
(median). Perhaps most surprisingly, the average pinner in
our sample has created over 1,000 pins. This probably is
due to our sampling strategy (i.e., sampling pinners based
0.5
0.4
people tend to connect with those similar to them (in this
case, of the same gender)? If this is in fact the case, then
men would immediately find themselves at a disadvantage
for repins since they would have smaller audiences. We feel
this deserves more work and a deeper analysis in the future.
female
Being American or British earns repins. While we were
unable to analyze every country in our dataset because of
sparsity, we do find that pinners from the United States and
the United Kingdom attract more repins than the rest of the
world, β = 0.104, p < 10−15 and β = 0.0773, p < 10−15 ,
respectively. We see this as a rich area for future research,
investigating the linguistic and cultural reasons behind the
attraction of content by American and British pinners.
density
0.3
male
0.2
R2-Connection: What structures connections?
0.1
0.0
0
5
ln(followers)
10
15
Figure 3. A comparison of follower count by gender, with orange representing females and gray representing males. While males have a lower
median, there is also more mass in the tail of the male follower distribution. We induced the distributions with Gaussian kernel density
estimates, a smoothing procedure.
on recently created pins); however, if our sample is biased
in this way, it does not invalidate our findings. At most, it
limits them to apply to the active sub-population of Pinterest
responsible for much of the content and behavior on the site.
Finally, our data support the popular and trade press view
suggesting that Pinterest has a female supermajority: 80% of
the pinners in our data are women.
Likes, comments, followers all drive repins. When we look
at the factors that drive pinners to repin each other’s content,
we see that likes, comments, gender, followers, following
count and total pins all contribute. Our model (shown in
Table 2) captures 44.9% of the variance around repinning;
while this is a fairly good explanatory model, including other
features in the model would presumably increase explanatory
power. We see some predictors going in expected directions.
For example, the more followers a pinner has, the more repins
they can expect, β = 0.000733, p < 10−15 . The more pins
a pinner creates, the less likely any one of them will be
repinned, β = -0.0000124, p < 10−15 . And as is to be expected,
more likes and more comments are strongly correlated with
the likelihood of repinning, β = 0.527, p < 10−15 and β =
0.22, p < 10−15 , respectively.
Being female means more repins. However, the result that
stands out most in Table 2 is that pins from females receive
dramatically more repins, β = 0.0801, p < 10−15 . While
smaller in effect size than likes and comment count, gender ranks as the third most powerful predictor and is two
orders of magnitude bigger than the next largest, a pinner’s
follower count. Do men pin content that is less interesting to
the broader Pinterest community? Or could assortativity [23]
be at work, a pattern often found in social networks whereby
In this paper, we examine the accumulation of followers as
a function of other activity metrics. Again, we see similar
patterns as we saw with repins: pins, following count and
gender all impact follower count, and in roughly the same
order (in terms of magnitude).
Being female means fewer followers. Most surprisingly,
while female Pinterest users get more repins, they get fewer
followers: β = -1.77, p < 10−15 . This finding seems perplexing. Why would the signs of the coefficient reverse in
the two regressions? Figure 3 provides some insight on this
point. First, as we would have suspected given our conjecture
about assortativity above, men have a lower median follower
count than women. However, the mean is substantially higher,
reflected in Figure 3 in the tail down the right side of the
axis. We see two possible explanations for this finding. First,
simple statistical artifacts could be at work: because four
times as many women as men use Pinterest, the variance of
the male distribution is bumpier, wider and noisier. If this is
true, the simple passage of time (and more men joining the
site) will make the distributions even out. However, possibly
the difference we see is due to other factors and will persist.
For example, maybe the genders are at different places along
the technology adoption curve, with proportionally more
early adopters among men (who disproportionately attract
attention)? Or, in a parallel argument, perhaps the men on
the site disproportionately attract followers simply because
they are scarce. Further research is needed to produce a more
conclusive answer.
R3-Comparison: How do Pinterest and Twitter compare?
We selected Twitter as a foil for Pinterest for multiple reasons.
First, we have seen quite a bit of scholarly work on Twitter in
recent years, and this can act as a backdrop for the findings
we present here. Second, the two sites share many of the same
structural affordances, notably they both adopt a following
& follower social network model to structure interactions,
users can repost (retweet or repin) other users’ contributions,
and references to external content are either frequent (URLs
on Twitter) or mandatory (on Pinterest, each pin refers to an
image and its containing web page).
Pinterest’s verbs: use, look, want, need. We performed a
textual analysis comparing what users say on Pinterest to
what they say on Twitter. The differences are illuminating,
this
do
!
!
much
money
as
g e t
e v e y
r
,
to
r a d
e
!
)
s o n g
id e a s
rf m
o
wh a t
y o u
w a lk
d o wn
t e
h
a is le
to
,
rf m
o
t e
h
rif t
s
d a n c e
to
y o u r
f h
a
t e r
d a u g h e
t r
d a n c e
a n d
s o
s o me
n ig h t
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rt a s
e
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t e n
h
,
re
e c
t
.
wa
r p
e a c h
t e n
h
t e y
h
y e a r
a n d
c a n
h a n g
t e ri
h
b a c k p a c k
a n d
c o a t
o n
t e
h
h o o k s
!
g e
r a t
id e a
!
r u s a b e
e
l
'ti
s
s o u n d s
i c e
n
r d b
i e
l
a n d
d e lic io u s
i
t s
h
i
t he
b a c k y a d
r
t x a s
e
my
h a ir
day
rf e z e
e
i
n e e d
t s
h
i
t n
h
i k
.
t e
h
n e e d
p h o o
t g
c h s
ir m
t a s
choose
because
v id e o
one
!
h u s b a n d
w il
s a y
'i
m
u s in g
u p
h is
p n
ir e
t r
in k
# o
li v e p h o o
t o
j o
j
!
i
v e y
r
-
i
f v o e
a
tir
want
s c p
ir u
t e
r s
you
.
i
want
all
of
you
dav e
,
f or ever
ra ms e y
,
cookie . . . 1 tbsp .
,
f v o e
a
tir
q u o e
t s
,
o r
f v o e
a
tir
s o n g s
.
.
.
!
these
!
ever yday
ta l k s
.
lik e
you
about
th i s
.
.
.
v e ry
c ool
h o w
and
m e…
butter
o k a y
t hese
of
this
ever yday
.
"
-
t he
not ebook
scr ipt ur e
, melted 1 tbsp . white sugar 1 tbsp . brown sugar 3 drops of vanilla
b u t
t s
h
i
.
i
wa n t
t e s e e e
h
rf e z e
e
fo r
a
wa n t
.
:
c a k e
h o w
f or
to
ma k e
s u mme r
fu rn i tu re
?
s o
th i s
we b s i te
has
p l e n ty
of
eas y
to
fo l l o w
plans
and
p ro j e c ts
to
b e a u ti fy
y our
h o me
fo r
c heap
!
pin
now
,
re a d
l a te r
.
:
)
i s a
n
t g a
r m
f w
e
t hem
t s
h
i
it
anot her
banana
pudding
r ecipe
,
ever
.
.
.
ti s
'
i o
n
t
t he
s o me h
t n
i g
lik e
!
d o l
!
this
best
!
r c e p
e
i e
t s
h
i
b u t
i
want
want
t a t
h
to
1 9 5 0 s
t s
h
i
a e
ir a
l e
ir l
.
i
wa n t
p o lk a
n ik e
in
i
one
!
it
t he
g a me
k in d a
a e
tf r
s o o n
!
a s a p
!
wo k
r o u t
lis t
i
wa n t
t b a c o n
a
!
to
!
!
r me mb e r
e
.
i
a
d e o
t x
a
n e e d
t s
h
i
il a
b
r a
ir n
h e re
.
in
!
!
!
# p a u a
l d
r e n
y o u r
her
when
i
t e y
h
t his
g o
r w
u p
.
c o p y c a t
or ganized
t he
queen
a b le
of
to
b a b
r e
i
e a c h
a t
t e
h
w e d d in g
:
t e
h
g o
r o m
a n d
t e
h
b e s t
ma n
wh
ti
t s
h
i
.
rif t
s
t k e n
a
hear t s
d o
lik e
d o
d a y
o f
p a
r y n
i g
s c h o o l
ir h t
g
.
b e o
f e
r
h e
w a lk s
to
t e
h
a e
tl r
.
v e y
r
me mo a
r b e
l
,
me a n n
i g u
f l
,
a n d
mo v n
i g
.
p o
r b e
l m
t e
h
to
there
h e e
r
.
.
.
n e e d
.
wa n t
ik e a
here
!
k o e
r a
!
d e v e
li d
t k a c h h
a
i o
lik e
back
t her e
to
!
,
b u t
in
it
s k n
i g
.
this
remember
. . it helps you come up with ways to arrange it
this fall !
one
a t
th e s e
know
t e y
h
o p e n
g a b b y
some
put
m a il
to
ma k e
a
go
a n y
,
a
make
be
,
o n e
♥
n e x t
they
t hat
wh
ti
want to
n o e
t s
p ic k
:
to
b u t
try
lti
e
k id s
a t
it
for
i
as
t e m
h
le t
t e
h
.
in
one
earn
it
lik e
n e e d
c le a n
s o
le a v e
a n d
b u c k e y e
o mg
c a me a
r
wi th
and
!
(
i
a n d
wa
r p
,
i
lo v e
f r
o
t o s e
h
d a y s
y o u
ju s t
wa n t
to
ma k e
o n e
c o o k e
i
p u mp k n
i
.
.
.
.
!
rt n
a
i
1
t s p
b
.
b u e
t r
,
me e
tl d
1
t s p
b
.
wh e
ti
s u g a r
1
t s p
b
.
b o
r wn
s u g a r
3
d o
r p s
o f
v a n a
li
p in c h
o f
s a lt
1
e g g
y o lk
1
/
4
c
p u mp k n
i
p e e
fr c t
c h s
ir m
t a s
!
th e re
a re
s o me
g re a t
ti p s
i
!
h o me ma d e
the easiest way to cook corn on the cob ? throw it in the oven at 350° for 25 - 30 minutes . that s it . leave the husk on , it will trap in the moisture , leaving you with juicy , tender corn
what
t p e
y
wh e e
r
y o u r
h e a tr
have
h o me ma d e
work
live
t e
h
is
,
lo o k
f r
o
piz z a
if
m or e
kids
a
b u c k e t
to
do
all
t he
f un
st uf f
wit h
t he
lit t le
ones
and it
in
th i s
wom ans
'
!
(
h e re
eat
see
just
!
}
s h e
h a n d u
f l
b e
r a k a
f s t
on
tells you
.
n e a r
it
p e a n u t
b u e
t r
c o o k e
i s
k id
r n n n
u
i g
f r
o
b e
r a k a
f s t
?
house
or
have
easy
her
or ganize
m ine
you
t his
.
?
ex ercises
.
.
cant
beat
t hat
is
am azing
,
r idiculous
,
over whem
l n
ig
!
c an
.
.
.
do
.
all
at home
of
it
!
!
dollar
a w e s o me
i
!
p e e
fr c t
“ o m
d
r
t r ee
it em s
.
look
at
t his
blog
,
lot s
of
gr eat
st oar ge
/
or ganz
in
ig
ideas
!
wa n t
s o
t n
ir g
le s s
h e a h
tl e
i r
a
lti
e
a o
r u n d
a c n
it g
lik e
a
d in o s a u r
.
n e e d
t si
h
lo o k
e a c h
get
stay
o h
t e r
in
r id
tif r
e
,
a n d
s o
rt n g e r
,
y o u
n e e d
to
rt n
a
i
y o u r
c o e
r
lik e
track
fo rtu n e
in
cut e
int o
,
a
look
keep
paint
wear
forget
take
r a
o
t y
r
of
f s e
a
t r
b e e
t r
on
spend
a
,
r n n e r
u
books
.
.
here's
on
.
t s
h
i
a
nice
d ri n k s
like
?
treat
pour
.
s ugar
y our
v odk a
in
an
e mp ty
l i s te ri n e
b o ttl e
,
is
not
add
3
an
added
d ro p s
of
i n g re d i e n t
blue
.
fo o d
3
ma s h e d
c o l o ri n g
and
bananas
1
d ro p
s p ra y
a
(
ri p e
of
)
,
1
/
3
c up
apple
s auc e
g re e n
,
2
c ups
o a ts
,
1
/
4
c up
a l mo n d
.
wa
mi l k
-
la
!
,
1
you
/
now
2
have
c up
a
5t h
ra i s i n s
of
vodka
p e tro l e u m
jelly
(
o p ti o n a l
to
use
il h s
g
t
s o
f r
o
f u
u
t e
r
r e
e
f e
r n c e
.
.
s o me
g e
r a t
id e a s
.
s u
t f
my
mo m
d id
a n d
s lit
d o e s
f r
o
me
!
e v e r
.
t si
h
.
o n
to
t e
h
g y m
c h a k
l b o a d
r
it
!
'i
m
a
lo n g
t e ri
h
t o s
o
l
this
remember
!
!
;
(
p le a s e
k e e p
t s
h
i
need to
t he
a
f ut ur e
b a lo o n
of
y our
since
helium
fre s h
is
not
a
p u mp k i n
daily
r enewabe
l
is
a
sour ce
and
or
is
c oat
in
th e
such
shor t
ins ide
supply
w
/
to
k eep
c u e
t
.
re mo v e
s n e a k y
wa y s
a
to
s p l i n te r
b u n
r
e x a
rt
eas ily
c a o
l e
ir s
by
wh e n
apply ing
so
d u n
ir g
t e
h
p l a n ti n g
!
d a y
a
many
,
wh
ti o u t
p a s te
of
bak ing
e x a
rt
rt n
a
i n
i g
s oda
wa te r
it
i thought were
!
-
-
-
i
d e n
if e
ti y
l
my
sweater
and
lo v e
clothes
a n y
goodwill
p a ti o
,
?
ruined
wa n t
to
r me mb e r
e
p o ts
!
b lu e
re
h a v e
!
.
.
th e n
wa i ti n g
s e v e ra l
,
t e e
h
r
a e
r
s o me
mi n u te s
n e e d
. . . chalk
t e s e
h
fo r
will remov e
g e
r a t
it s
p
th e
s p l i n te r
grease
to
pop
s tains
out
of
th e
from c lothes
!
s a
rt w b e e
ir s
b e a c h
wa n t
m in k a
f r
o
'ti
s
it e
m
to
my
p a in t
!
k id s
to
wa k e
u p
to
o n
t e ri
h
b h
tri d a y s
!
i
y e a h
o e
tf n
.
t e s e
h
!
lo l
n e e d
to
k n o w
w o ma n s
'
.
.
to
r me mb e r
e
i
me a n
t s
h
i
-
ti o
s
t e
h
d o m
r
my
h u n e
t r
k c
ti h e n
-
e v e y
r
y o u
s o
to
r o m
o
r me mb e r
e
t s
h
i
!
c a b n
i e s
t
.
i a
n
t c t
a n c h o r
)
2
.
c o mme n t
o n
t s
h
i
im a g e
to
le t
u s
w e ig h t
k n o w
y o u
a e
r
in
.
3
.
c e
r a e
t
a
b o a d
r
n a me d
"
i
wa n t
a
rf e
e
f h
h
f
d e
r s s
"
.
4
.
p in
a t
wh
ti o u t
i
!
n e e d
a n
:
h o me ma d e
rf ti
u
my
o mg
s it
ju s t
p a n e a
r s
'
.
.
.
i
mu s t
n e e d
'i
m
wa e
t m
r e o
l n
ma g n e c
it
i
!
n e e d
o n e
!
-
-
do
!
every
week
.
.
now
i
b e n c h
of
have
t hese
.
a
printable
to
i
# s lo w c o o k e r
# e a s y d e s s e tr
b g
ir h e
t n
b o y s
use
!
!
c o n c e
r e
t
f r
o
i
o u r
d o n t
h g
i h y
l
about
y e lo w
s p ra y
p a i n ti n g
c anning
,
fil
e
fu rn i tu re
!
d e h y d a
r n
it g
.
,
@s a a
r b e h
t c o o k
rf e z n
e
i g
a n d
o h
t e r
n e a t
irt k s
c
e g g le s s
.
s e
t p
# d is n e y
i
wa n t
to
d u a
r b e
l
k e e p in g
g e n
it g
to
s a
t tr
fo r
l a te r
mo n e
t s s o ir
f eeding
a
t v
h
ir e
.
t his
how
to
fi x
y our
iphone
a fte r
cr owd
wa te r
on
t he
d a ma g e
-
cheap
b e tte r
.
includes
s a fe
a
th a n
huge
-
m enu
in g
idea
s o rry
a t
list
h o me
,
t a s
h
't
a
char t
f or
what
to
ser ve
depending
on
what
t im e
of
day
your
par t y
is
at
,
inexpensive
ways
to
add
som e
pizaaz
to
your
f ood
t able
,
f ood
shoppn
ig
t ips
,
c o o l
s o me
y o u
o wn
a
h o me
.
w il
i
n e e d
.
to
i s e
n
t a d
these
wedding party bottle cozy . these are cute , i need to get these for my reception bar .
for my reception bar .
a
th e
th i s
f n c y
a
body
you
d e s e
ri
.
wh
ti
t s
h
i
s y e
t m
wh e n
my
h a n d s
o n
be
!
!
my
s e t
list
wh
ti
get
d e s s e tr
o r
f r
o
)
!
if
s o me o n e
d e lic io u s
(
a
my grocery
one
know
wh e
ti
wa n t
fo r
these
a s a p
mn
i a
i u
t e
r
r me mb e …
e
r
p e
r y
t
n e e d
this
t s
h
i
i n
n
if y
ti
w o ma n s
'
.
!
i
make
o n
s o me
t e
h
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a n d
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learn
how
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c o
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t e
h
a tr
ever waste fresh herbs
doing
start
again !
♥
wa n t
l'i
fit n y
a
h o ly
wh e
ti s
t his
ma k n
i g
bring
.
take
unpack
check
n e e d
!
!
c u a
tr n
i
:
n e e d
r c p
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go
buy
ins ide
im por t ant
s o
t s
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read
survive
have
.
4 0
.
!
o h h
lo s e
this
try
!
.
a e
r
wa k e
t e
h
t x t
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th e
:
in
p in
on
ma d e
.
for
t si
h
wa te r
year
. .
t s
h
i
.
p in
and
h o w
e a c h
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bleac h
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re
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k ids
mo e
r
o n
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a l
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ma k e
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.
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i
1
my
of
is
n o v e mb e r
in
to
want
mi x tu re
s u p e r
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s i te
in
-
g o t
.
-
:
las t
next
wh e n
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c a rv i n g
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one
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h
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ma k e
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s h o we h
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o u r
sil e
t n n
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this
e v e y
r
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homeownership
f un
yourself , do
this
like
t his
.
laught er
d a y
s a
rt w b e y
r
to
.
.
.
i
wa n t
my
ju s t
pr oduces
t si
h
m or e
f ond
.
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s h o p p n
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m em or ies
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s a e
f
.
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a n d
t p c
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f m
o
r a l
/
c a
l s s c
i a l
i can
w e d d in g s
.
a d o a
r b e
l
not stop
rubbing
my
legs
together
. it feels
like
i paid
for
that ov er
expensiv e
pedicure
at the
salon
.
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r d e
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ma k e
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o
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.
.
c h a tr
to
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t a
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t
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be
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pinned
th i s
.
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p lu n g e
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e v e y
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t n
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c e
r p e
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n
t e
r s n
it g
Figure 4. Word Tree visualizations of searches in the Pinterest text dataset. At top, a visualization depicting a search for “want to”. The visualization
shows phrases that branch off from “want to” across all the text in the Pinterest dataset. A larger font-size means that the word occurs more often.
“want to” appeared 2,614 times. At bottom, a Word Tree visualization of a search for “need to”, a phrase that appeared 2,878 times. Both “want” and
“need” are among the top textual features for distinguishing Pinterest from Twitter. The visualizations come from the site Many Eyes.
providing a concise set of terms that serve to identify Pinterest. Some describe topics that enjoy considerable attention on
the site, like DIY (β = 3.003), recipe (β = 1.745), book (β =
0.506), photo (β = 0.363) and cup (β = 2.427), another manifestation of recipe. Still others depict qualities that set the site
apart: old (β = 0.671), fun (β =0.156) and pretty (β = 0.721).
Of the 34 words listed in Table 4, only four are verbs in
their primary sense, however. They are use (β = 0.969), look
(β = 0.31), want (β = 0.173) and need (β = 0.0916). Many
popular press articles have focused on Pinterest’s commercial
potential, and here we see verbs illustrating that consumption
truly lies at the heart of the site.
Twitter: lol, watching now. Turning to the language of Twitter, we something altogether different. First and foremost,
words jump out proclaiming the importance of now (β =
0.0916), such as today (β = -2.213), tonight (β = -2.403)
and time (β = -0.545). This perhaps simply reflects Twitter’s “What’s happening?” prompt above the box for entering
tweets. We also see words that proxy for underlying demographic differences between the sites, such as the Spanish
words que (β = -3.7) and la (β = -1.694). Given previous
research on Twitter’s importance surrounding currently unfolding events [25], we unsurprisingly see words like watching (β = -2.736), see (β = -0.989) and going (β = -1.273)
acting as strong predictors for Twitter over Pinterest. We also
see some very social words in Twitter’s lexicon, like thanks
(β = -2.849), lol (β = -1.193), :-D (β = -0.959) and haha
(β = -0.906), all mimicking processing we would normally
associate with face-to-face social life.
Limitations and Future Work
Our work has several limitations, which in turn suggest avenues for future research.
Obtaining data. The way we obtained a sample of Pinterest
data was fairly labor-intensive and doesn’t offer a guarantee
of randomness. For example, the fact that the average pinner
in our sample had 1K pins suggests that we were sampling
from the high end of the activity distribution. While we believe our results still stand, we obviously would prefer to
obtain a random sample. Clearly the best way for researchers
to be able to obtain appropriate data samples would be for
Pinterest to publish an API.
Data analyzed. We did not sample the main type of content
that drives Pinterest activity: pictures. The count and text
data we did sample is more analytically tractable: there are
scalable, efficient and validated methods for such data, but
no analogous methods for analyzing image data. For example, recognizing objects in images (which certainly would
be useful for understanding Pinterest behavior) without an
extensive training set remains an unsolved computer vision
problem. In fact, simply generating the training set has served
as the motivating problem for influential work in human computation [31]. We see analysis of pictures—whether through
automated or human-computation techniques—as an important area for future research. Another way to get at some
of the same issues would be to analyze the external web
pages that contain images from pins; for example, it would be
interesting simply to identify the proportion of pins that link
to e-commerce sites and which e-commerce sites are most
popular.
Methods used. Our approach is wholly quantitative and statistical. Such methods have built-in blind spots: most significantly, while they permit making claims about broad,
large-scale practices on the site, they cannot uncover users’
motivations and goals for using Pinterest. Qualitative research
can be used to enrich the picture we painted in this paper, providing thick descriptions of motivations and goals for using
Pinterest. Further, the statistical techniques we used examine
only a small segment of the possible behaviors on Pinterest.
As we mentioned above, for instance, our methods ignore
photo-sharing practices themselves. With current technologies, to take one example, you could examine the distribution
of color palettes as they vary with popularity indicators. We
look forward to both quantitative and qualitative work that
follows up on the findings we obtain in this early study of
Pinterest.
Additional analysis of location. While we represented location in terms of countries, it may be productive to explore a
more detailed representation in the future, for example, one
that enables reasoning based on geographic proximity. For
example, we would like to investigate locality of connection:
say, to what extent do pinners from the UK follow other UK
pinners, repin their pins, etc.?
Additional analysis of gender. It would be interesting to do
further analysis of the effects of gender on Pinterest interaction; indeed, we plan to do so. This would strengthen the
contrast to findings from male-dominated sites like Wikipedia.
For example, we would like to know whether men and women
connect more to other users of the same gender or another
gender, whether women and men are more likely to repin
pins from users of the same gender, whether men and women
favor different vocabulary terms in their descriptions and
comments, and whether men and women focus on different
topics in their pins and comments.
CONCLUSION
In this paper, we provide a statistical overview of Pinterest, a
new social network site surging into prominence. We arrive
at three main findings. First, being female on the site leads to
more repins, while geography seems to play no role. Second,
women have fewer followers. Finally, comparing the language
used on Pinterest to language used on Twitter, we come to a
concise set of terms defining the two sites. Notably, the four
verbs uniquely describing Pinterest are “use,” “look,” “want,”
and “need,” reflecting the “things” at the heart of Pinterest.
We hope these early findings act as springboard for future
research on Pinterest.
ACKNOWLEDGEMENTS
We would like to thank our reviewers and our respective
research groups for making valuable comments that improved
this work. We also thank the NSF for supporting this research
with grant IIS-1212338.
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