=Paper=
{{Paper
|id=Vol-1622/SocInf2016_Paper3
|storemode=property
|title=Pinpointing Influence in Pinterest
|pdfUrl=https://ceur-ws.org/Vol-1622/SocInf2016_Paper3.pdf
|volume=Vol-1622
|authors=Panagiotis Liakos,Katia Papakonstantinopoulou,Michael Sioutis,Konstantinos Tsakalozos,Alex Delis
|dblpUrl=https://dblp.org/rec/conf/ijcai/LiakosPSTD16
}}
==Pinpointing Influence in Pinterest==
https://ceur-ws.org/Vol-1622/SocInf2016_Paper3.pdf
Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
Pinpointing Influence in Pinterest
Panagiotis Liakos1 , Katia Papakonstantinopoulou1 , Michael Sioutis2 ,
Konstantinos Tsakalozos3 , and Alex Delis1
1
University of Athens, GR15703, Athens, Greece
{p.liakos,katia,ad}@di.uoa.gr
2
Université d’Artois, CRIL UMR 8188, Lens, France
sioutis@cril.fr
3
Canonical Group Ltd., London, UK
konstantinos.tsakalozos@canonical.com
Abstract. The success of most applications that run on top of a social
network infrastructure is due to the social ties among their users; the
users can get informed about the activity of their friends and acquain-
tances, and, hence, new ideas, habits, and products have the opportunity
to gain popularity. Therefore, understanding the influence dynamics on
social networks provides us with insights that are useful in designing ef-
ficient social network applications. In this work we focus on Pinterest, a
social network that is often used to promote commercial products, and
investigate the influence mechanisms in it. We examine the user inde-
gree and PageRank as potential estimators of the number of repins and
likes the user may receive. We observe that, although both measures are
weakly associated with user influence in Pinterest, PageRank is much
more powerful than indegree in revealing how much influential a user is.
Keywords: Social Influence; PageRank; Pinterest.
1 Introduction
Over the past two decades the rise of social networking sites has shaped new
forms of interaction. Social media such as Facebook, Twitter, and Pinterest are
drawing millions of individuals, who now depend on them to keep up with friends,
follow breaking news, share their interests, and discover products or events. The
popularity of online social networks and the diversity of the activities of their
users make them ideal candidates for studying influence patterns. In particular,
we want to find out whether certain individuals in a social network have the
power to affect their social contacts and are in the position to convince them to
buy a product or adopt a political idea.
The diffusion of influence has received significant attention from the fields of
sociology, advertising, and political science. Early studies argue for the existence
Copyright c 2016 for the individual papers by the papers’ authors. Copying per-
mitted for private and academic purposes. This volume is published and copyrighted
by its editors.
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
of a small minority of opinion leaders, that are able to persuade the majority
of the society to mimic their behavior [6]. More recent studies, however, do not
outright accept this hypothesis. In [4] the authors consider the probability of a
customer buying some product as a function of the influence of other customers,
as well as her intrinsic desire for the product. In [16] it is suggested that although
an influential minority may exist, it is seldom responsible for spreading ideas; it
is a critical mass of easily influenced individuals who trigger chain-reactions of
influence. Identifying influential individuals allows for cost-effective viral mar-
keting techniques to increase brand awareness or even sway the public opinion.
The massive activity of online social network users enables us to collect rich
large-scale data and assess the presence and potential of influential users. A re-
lated recent effort [2] investigates influence in the Twitter social network. Topo-
logical measures, such as the number of one’s followers (indegree), are reported
to fail in exposing user influence. This is attributed to the users’ tendency to
follow others for courtesy and is often referred to as the million follower fallacy.4
However, user activity varies across different social networking sites. Thus, it is
important to examine whether indegree reveals user influence in other social me-
dia. Moreover, considering more refined measures of importance implied by the
network topology may lead to more effective identification of influential users.
We focus on Pinterest, an image-based online social network in which users
can post (or pin) content they find interesting and browse the content of others
in their feed, where they can re-post (or repin), comment, or endorse (like) other
pins. Consequently, users focus on the curation and discovery of existing content
and many businesses opt to join Pinterest in an effort to promote their products.
We perform an in-depth empirical analysis to measure the extent to which the
indegree of Pinterest users is associated with the number of repins and likes
they receive. In addition to this, we propose the use of PageRank [8] to better
estimate one’s potential to influence others. Indegree considers the expressed
opinions of all users as equally important. In contrast, PageRank distinguishes
important users based on the network topology. Our intuition is that the more
authoritative a user is, the more influential she is expected to be. Therefore, the
use of PageRank to identify influential users sounds promising. In summary, we
make the following contributions:
– We examine the association between indegree and user influence in a very
popular social network. We find that, similarly to Twitter [2], the indegree
of Pinterest users reveals very little regarding the number of repins and likes
a user may receive.
– We propose the use of PageRank for identifying influential individuals and
investigate its effectiveness. Our findings suggest that, even though the as-
sociation between PageRank and user influence appears limited, PageRank
is much more powerful than indegree in revealing the influence of a user.
The rest of this paper is organized as follows. In Section 2 we review the re-
lated work. Section 3 provides an insight into Pinterest’s fundamental elements
4
http://bit.ly/1WaTAeK
27
Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
and outlines our approach. In Section 4 we present an empirical analysis of Pin-
terest activity to study the extent of influence in the network. Finally, Section 5
concludes this work.
2 Related work
The following fundamental algorithmic problem for social network processes
dealing with influence was posed by Domingos and Richardson in [15,4]: if we
can try to convince a subset of individuals to adopt a new product or innova-
tion, and the goal is to trigger a large cascade of further adoptions, which set
of individuals should we target? This problem is studied in [7] in the context of
the most popular models in social network analysis. The authors show that the
aforementioned problem is essentially the optimization problem of selecting the
most influential nodes, which is N P-hard, and provide the first provable approx-
imation guarantees for efficient algorithms. The results of this study gave rise to
a number of works that try to identify influential users by employing heuristics
that are based on real network data.
In [2], Cha et al. study user influence on Twitter; based on a Twitter crawl,
they compare three measures of influence, namely, indegree, retweets, and men-
tions, and observe that users who have high indegree, although popular, are not
necessarily influential in terms of causing retweets or mentions. Their findings
suggest that topological measures alone, probably do not reveal much about how
influential a user is. However, they leave room for examining more refined mea-
sures of influence, which do not consider all links in a network to be transferring
the same authority. Weng et al. [17] focus on the problem of identifying influen-
tial users of micro-blogging services, with Twitter being their service of choice for
studying such influence. In a dataset that they prepared for their study, they ob-
serve that 72.4% of the users in Twitter follow more than 80% of their followers,
and 80.5% of the users have 80% of users they are following follow them back.
Their study reveals that the presence of such reciprocity can be explained by
the phenomenon of homophily [11], i.e., the tendency of individuals to associate
and bond with similar others. Based on this finding, they propose TwitterRank,
an extension of the PageRank algorithm, to measure the influence of users in
Twitter. Identifying patterns of influence also serves the purpose of evaluating
sociological models. In another line of research, in [10] the authors study real
user activity in the Digg social network, based on sociological studies and the
game theory framework, and identify a model that closely describes the observed
influence. In particular, they model user activity as an opinion formation game
in which each user is influenced by her social contacts and find that the Nash
equilibria of the game are nice illustrations of how users real behave.
Gilbert et al. [5] focus on identifying certain properties of Pinterest. In partic-
ular, they try to answer the questions of what drives activity on Pinterest, what
role gender plays in the site’s social connections, and, finally, what distinguishes
Pinterest from other networks, such as Twitter. With respect to those questions,
they conclude that being female implies more repins but fewer followers, and
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
that four verbs set Pinterest apart from Twitter, namely, use, look, want, and
need. Similarly to [5], properties of Pinterest are also highlighted in [3]. Chang
et al. study a fundamental issue for social curation sites where people collect,
organize, and share pictures of items, with a focus on Pinterest, as it is the most
prominent example of such a site. In particular, they study the issue of what
patterns of activity attract attention. They organize their study around two key
factors, namely, the extent to which users specialize in particular topics, and
homophily among users. Further, they also consider the existence of differences
between female and male users. Their study reveals that women and men dif-
fer in the types of content they collect and the degree to which they specialize.
These findings suggest strategies both for users (e.g., to attract an audience)
and for maintainers (e.g., to explore content recommendation methods) of so-
cial curation sites. Mittal et al. [12] characterize Pinterest on the basis of large
scale crawls of 3.3 million user profiles, and 58.8 million pins. In particular, they
explore various attributes of users, pins, boards, pin sources, and user locations
in detail, and perform topical analysis of user generated textual content. This
characterization revealed the most prominent topics among users and pins, such
as design, fashion, photography, food, travel, music, and art. Moreover the top
image sources and geographical distribution of users on Pinterest were obtained.
Ottoni et al. [13] make a first attempt towards a more complete understanding
of user behavior across multiple online social networks, such as Twitter and
Pinterest. They collect a sample of over 30, 000 users that have accounts on
both Twitter and Pinterest, by crawling their profile information and activity
on a daily basis for a period of almost three months. Then, they develop a
novel methodology for comparing activity across these two sites, that builds on
the Labeled Latent Dirichlet Allocation model (L-LDA) [14], a supervised topic
model for credit attribution in multi-labeled corpora. The authors find that the
global patterns of use across the two sites differ significantly, and that users tend
to post items to Pinterest before posting them on Twitter. These findings can
assist in the understanding of user behavior on individual sites, as well as the
dynamics of sharing across the social web.
3 Tracing Influence in Pinterest
In this section we discuss the Pinterest social network; its immense popular-
ity, the actions allowed to its users, and its most prominent categories indicate
that Pinterest is extremely interesting business-wise. Moreover, we detail the
measures of influence that we will employ in our empirical analysis.
3.1 Understanding Pinterest
Pinterest is an image sharing social network that was founded in 2010 and was
the fastest site to surpass the 10,000,000 monthly active users milestone.5 During
5
http://techcrunch.com/2012/02/07/pinterest-monthly-uniques/
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
the year 2015, Pinterest was also reported to have broken the mark of 100,000,000
monthly active users.6 Notably, the vast majority of these users are female,7
which makes Pinterest particularly interesting to study.
Pinterest users are able to create a profile along with one or more boards
where they may pin images or other media content, for example videos. They are
also able to follow other users or specific boards in order to receive personalized
updates in their feed. Pins can be liked or repinned (shared) by other users, as
it is also the case with the content of other social networks such as Facebook or
Twitter.
Pinterest boards are organized into a broad range of categories, typical ones
being Food & Drink, Weddings, and Home Decor. These categories are indicative
of the users’ habit of creating digital shopping lists of products they are interested
in buying. This tendency has attracted significant commercial attention, as many
businesses invest in creating compelling boards to increase their revenue.8
3.2 Defining influence in Pinterest
A user’s behavior in a social network is often affected by others. However, the
task of singling out those individuals who actually cause social influence in an
online social network is not trivial. In this paper, we focus on three fundamental
actions performed by Pinterest users, namely, follow, repin, and like. We consider
that the number of followers a user has, as well as the number of repins and likes
she receives, are indicative of her potential to affect others in the network and
can be used as a measure of influence.
The use of indegree as a quality measure may be flawed due to its local na-
ture. To alleviate this issue we additionally consider the use of PageRank values.
In information networks, with the World Wide Web being the most popular ex-
ample, the authority of each node is estimated using the PageRank algorithm [8],
a quality metric introduced by the creators of the Google search engine that is
based on the network’s link structure. According to the definition of PageRank,
a node’s authority is distributed uniformly to the nodes it has a link to. As a
consequence, a node is important if many important nodes link to it. The use of
PageRank, however, is not restricted to search engine optimization. PageRank
is used in recommendation systems, in ranking tweets in Twitter, and even in
suggesting friends in online social networks.
The measures of influence that can be drawn from the Pinterest network and
are examined in this work are the following ones:
– Indegree influence: the number of followers of a user directly indicates the
size of the audience of that user.
– PageRank influence: the PageRank of a user indicates the strength of her
influence on her followers.
6
http://mobile.nytimes.com/blogs/bits/2015/09/17/
pinterest-crosses-user-milestone-of-100-million/
7
http://www.omnicoreagency.com/pinterest-statistics/
8
https://business.pinterest.com/en/success-stories
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
– Like influence: the number of likes containing one’s name indicates the
ability of that user to generate popular content.
– Repin influence: the number of repins containing one’s name indicates the
ability of that user to generate content with pass-along value.
Notice that the first two measures depend solely on the topology of the social
network, while the other two take into account the user activity on the network
as well.
4 Empirical Analysis
This section details the analysis we performed on the Pinterest network and
our empirical observations concerning the influence patterns exhibited. We first
introduce the dataset we used for our analysis and the experimental setup needed
for the PageRank algorithm. Then, we proceed with answering the following
questions:
– Do the indegree of Pinterest users and the repins and likes they receive follow
long-tailed distributions?
– Is there significant overlap among the top users based on indegree or PageR-
ank and the top users based on the number of repins or likes they received?
– How strong is the pairwise rank correlation between indegree, repins and
likes in Pinterest?
– Is the rank correlation improved when we consider the use of PageRank
instead of indegree?
4.1 Experimental Setting
Our empirical analysis is based on the Pinterest dataset described in [19,18]. The
activity of the users was collected from January 3rd 2013 to January 21st 2013,
while the social graph was crawled during April 2013. We focused on the subset
of this dataset that contains only links that are common to both Pinterest and
Facebook. Our experimental setup comprises a social graph of 36,198,633 users
and 983,520,986 social ties. Regarding the activity of these users, we analyzed a
total of 18,957,340 repins, and 9,066,973 likes on pins. These pins were created
by a total of 1,253,189 users.
Calculating the PageRank values of large-scale graphs calls for the use of
Pregel-like graph processing systems like Apache Giraph.9 Using Juju10 , we de-
ployed an Apache Hadoop 2.7.1 cluster on a Dell PowerEdge R630 server with
an Intel R Xeon R E5-2630 v3, 2.40 GHz processor, and 256 GB of RAM. Then,
we built Apache Giraph in the client node of our cluster and submitted a Gi-
raph job that calculated the PageRank values of the users in our social graph by
performing 80 iterations. This allowed us to fully utilize our infrastructure and
obtain the desired results in less than 3 hours.
9
http://giraph.apache.org/
10
https://jujucharms.com/big-data
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
Indegree, Repin & Like Distributions
1e+07
Indegree
Repins
1e+06 Likes
100000
Number of Users
10000
1000
100
10
1
1 10 100 1000 10000
Indegree & Number of Repins/Likes
Fig. 1. Distribution of indegree and received repins/likes. Most of the Pinterest activity
is centered around a small number of users.
4.2 Distribution of indegree and received repins/likes
In Figure 1 we illustrate the indegree distribution of the social graph of Pinterest
as well as the distribution of influence based on repins and likes. We use a
logarithmic scale to highlight the fact that the users’ indegree, as well as the
users’ number of received repins or likes, varies by several orders of magnitude. In
particular, we observe that there are a few users with more than 1,000 followers,
repins or likes, whereas the majority of users have no more than one follower and
have received no more than one repin or like. Therefore, most of the activity on
the network is centered around a small minority of users. Moreover, we observe
that users tend to receive slightly more repins than likes.
4.3 Overlap of top-ranked users
We continue our analysis of the Pinterest dataset by examining the overlap of
the top-ranked users for each measure. In particular, the Venn diagram of Fig-
ure 2(a) depicts the overlap of the top-10,000 users according to their indegree,
the number of received repins, and the number of received likes. We observe that
the overlap of indegree with both of the other measures is marginal. In contrast,
there is significant overlap between the users whose pins received many repins
and those whose pins received many likes.
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
Indegree PageRank
9,955 9,990
9 11 3 2
25 5
3,751 6,215 3,749 3,757 6,235 3,758
Repins Likes Repins Likes
(a) (b)
Fig. 2. Venn diagrams of the top-10,000 users across indegree/repins/likes (a) and
PageRank/repins/likes (b). The overlap of indegree and PageRank with repins and
likes is marginal. In contrast, repins and likes are strongly correlated.
Moreover, we present the corresponding Venn diagram for the top-10,000
users according to their PageRank in Figure 2(b). We see that the overlap of
PageRank with repins and likes is even more insignificant in this case.
These observations hint that there is very weak correlation of the indegree or
PageRank of users with the frequency they receive repins and likes. In addition
to this, we showed that users who receive many repins tend to receive many likes
as well.
4.4 Comparing Influence Measures
For each of the 36, 198, 633 users of our dataset we calculated the value of in-
degree and PageRank and the number of repins and likes, and adopted the
methodology of [2] to quantify the association between them. In particular, we
characterized our comparison on the relative order of users’ ranks as a measure
of difference. We assigned the rank of 1 to the most influential user, and in-
creased the rank as we proceeded to less influential users. Identical values were
each assigned fractional ranks equal to the average of their positions in the as-
cending order of the values [1]. Then, we used the Spearman’s rank correlation
coefficient [9], a non-parametric measure that is used to assess the degree of asso-
ciation between two variables, to examine whether two ranked variables covary.
This measure is especially useful as it does not make any assumptions regarding
the distribution of the data.
The Spearman’s rank correlation coefficient is defined by the following for-
mula:11
6 d2i
P
ρ=1−
n(n2 − 1)
where di = rg(Xi ) − rg(Yi ) is the difference between the two ranks of user i, and
n is the total number of users.
11
In our case all ranks are distinct integers, hence, we present the simplified formula.
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
1
Indegree/Repins
PageRank/Repins
Indegree/Likes
Spearman's rank correlation coefficient
0.8 PageRank/Likes
Repins/Likes
0.6
0.4
0.2
0
Fig. 3. Spearman’s rank correlation coefficients for all the users of our dataset.
Figure 3 depicts the correlation between all pairs of measures examined in
this work, for all the users of our dataset. We observe that both the indegree and
the PageRank measures exhibit very weak correlation with the repin and like
measures. A weak correlation between topological measures and user influence
has already been reported in [2]. However, our results suggest that the association
is much weaker on Pinterest than on Twitter.
In [2], a preprocessing step is applied to the dataset that removes all users
with a limited number of tweets since the creation of their account. We investi-
gated the impact of an equivalent preprocessing step to our dataset by examining
only those users who have created at least 15 pins, i.e., a total of 20, 011, 513
users. This resulted to a weaker correlation of indegree with both repins (0.1068)
and likes (0.0997). Therefore, we attribute the deviation of our results from the
findings of [2] to the different patterns of use across Pinterest and Twitter.
Another important finding illustrated in Figure 3, is the extremely strong
correlation between repins and likes. This is indicative of the different focus of
Pinterest compared with Twitter; in Twitter the action of a retweet focuses
on the content of a tweet and the action of a mention on the user, whereas in
Pinterest both the action of a repin and the action of a like focus on the content
of a pin. As such, the correlation between the two corresponding measures in
Twitter is reported to be moderate (0.58) [2], whereas the two measures in
Pinterest are very strongly associated.
We have already discussed that most users have very few followers, as clearly
shown in Figure 1. Consequently, most users are tied when ranked according to
their indegree. This may lead to a fabricated result regarding the correlation of
measures, as users with low indegree most likely also have limited repins and
34
Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
1
Indegree/Repins
PageRank/Repins
Indegree/Likes
Spearman's rank correlation coefficient
0.8 PageRank/Likes
Repins/Likes
0.6
0.4
0.2
0
Fig. 4. Spearman’s rank correlation coefficients for the top 10th percentile of users
according to their indegree.
likes. To alleviate this issue, we considered users ranked in the top 10th and 1st
percentiles, respectively, according to their indegree, as suggested in [2].
We observe in Figures 4 and 5 that the correlation of indegree with repins
or likes is indeed even weaker for the top 10th and 1st percentiles of users re-
spectively. However, what stands out is that the correlation of PageRank with
repins and likes is stronger than that of indegree for these two cases. In particu-
lar, in Figure 4 we observe that for the top 10th percentile of users according to
their indegree, the association of PageRank with user influence is about twice as
strong as that of indegree. The results for the 1st percentile, depicted in Figure 5,
are even more impressive, as the statistical dependence with PageRank instead
of indegree is more than twice as strong.
This verifies our intuition that using PageRank instead of indegree allows for
capturing the importance of users more accurately. Given that the focus of both
repins and likes is on the content of the pin and not on the user who posted
it, we expect that PageRank will perform even better against actions that are
centered toward individuals, e.g., Twitter’s mentions.
5 Conclusion and Future Work
The study of influence patterns on social networks is essential for the design
of a successful advertising strategy. The recent unprecedented growth in the
number of online social networking services allows us to empirically validate
relevant theories and uncover opportunities for viral marketing techniques. We
performed an analysis of a large amount of activity on the Pinterest network. The
case of Pinterest is particularly interesting as it is embraced by an ever-increasing
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
1
Indegree/Repins
PageRank/Repins
Indegree/Likes
Spearman's rank correlation coefficient
0.8 PageRank/Likes
Repins/Likes
0.6
0.4
0.2
0
Fig. 5. Spearman’s rank correlation coefficients for the top 1st percentile of users ac-
cording to their indegree.
number of businesses eager to promote their products to a wide audience through
captivating pin-boards.
We examined the association of the indegree of Pinterest users with the
number of times their pins are repinned or liked. Our results reveal that there
is very little correlation between the ranking of users based on their indegree
and their ranking based on the number of either repins or likes they receive.
We attribute this result to the million follower fallacy that is evident in this
network, and the fact that the focus of repins and likes is more on the content
of pins rather than on the user who posted them. Furthermore, we proposed the
use of PageRank instead of indegree for the identification of influential users. As
PageRank is a measure that captures how authoritative a user is in a network,
we expect that a ranking of users based on their PageRank value will provide us
with a more accurate view of their potential to influence their social contacts.
Indeed, even though correlation with the ranking of users based on repins or
likes received is still limited, PageRank performed much better than indegree.
We will further investigate the influence patterns of the Pinterest network
with regard to the variance exhibited across different topics. In particular, we
will consider the categories of pins when ranking users based on the repins and
likes received. The overlap of influential users across different Pinterest cate-
gories will indicate how many are able to spread information over a variety of
topics. Moreover, we will examine whether PageRank proves to be even more
successful when quantifying its association with an action targeted towards the
user instead of the content (both repins and likes are related to the content of
a pin). We expect that for online social networks that focus more on original
content, like Twitter or Instagram, and with actions targeted towards the user,
36
Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
such as mentions, PageRank’s performance will be significantly superior to that
of the indegree.
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