Characterizing the YouTube video-sharing community

 
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Characterizing the YouTube video-sharing community
Characterizing the YouTube video-sharing community∗

                                    Rodrygo L. T. Santos, Bruno P. S. Rocha,
                                   Cristiano G. Rezende, Antonio A. F. Loureiro
                                             Department of Computer Science
                                             Federal University of Minas Gerais
                                            Belo Horizonte, MG 31270-901 Brazil
                               {rodrygo,bpontes,rezende,loureiro}@dcc.ufmg.br

ABSTRACT                                                          February 2005, YouTube was officially launched in Decem-
The YouTube video-sharing community is a recent and suc-          ber of the same year and has not stopped growing since then.
cessful phenomenon that provides an expressive representa-        By July 2006, the site reported to serve 100 million videos
tion of a social network. Despite its accelerated growth, a       per day, with a daily upload of more than 65,000 videos and
deep study of YouTube’s topology has not yet been made            nearly 20 million unique visitors per month – a 29% share
available. For this work, we have collected a representative      of the US multimedia entertainment market and 60% of all
sample of YouTube using our Crawlanga tool and analyzed           videos watched online [12]. Its storage demands were es-
both its structural properties, as well as its social relation-   timated at around 45 terabytes with several million dollar
ships among users, among videos, and between users and            expenses on bandwidth per month [3]. Within one year of
videos. We analyze properties such as profile of users and        its launch, YouTube was purchased by Google for US$1.65
popularity of videos in order to highlight the impact of social   billion in stock.
relationships on a content-sharing network.
                                                                  YouTube’s success can be seen as an example of the “wisdom
Categories and Subject Descriptors                                of crowds” [14]: the site exerts no control over its users’ free-
H.2.8 [Database Management]: Database Applications-               dom for publishing 2 , in such a way that users not only share
Data Mining; J.4 [Computer Applications]: Social and              their videos with a few friends, but instead participate in a
behavioral sciences                                               huge decentralized community by creating and consuming
                                                                  terabytes of video content, ranging from home-made stand-
General Terms                                                     up performances to eyewitness footages from inside news as
                                                                  they occur anywhere in the world.
Human factors, Measurement
                                                                  Despite its enormous popularity and the sums of money in-
Keywords                                                          volved, it is rather surprising that (at least to our knowl-
Virtual communities, network sampling, network analysis           edge) no study has been carried on unveiling the virtual
                                                                  community behind YouTube.
1.     INTRODUCTION
The last decade has witnessed the emergence of several pop-       In this paper, we present an analysis of YouTube network,
ularity phenomena through the word-of-mouth and self-pub-         based on a sample of it we were able to collect using a
lishing made feasible by the World Wide Web. This is true         crawler tool. In our analysis we focus users and videos,
for people, the content they produce, and the vehicles that       and attributes and relationships between them. We observe
distribute their production. Some of these phenomena have         attributes such as number of videos visualizations, users sub-
declined or have been replaced as rapid as they rose, while       scription, users favorite lists, commenting, and others. We
others have retained a steady pace of growth.                     also model the collected network as different networks in-
                                                                  cluding specific views as, for instance, a friendship network
The TIME’s Invention of the Year for 2006 [4], the YouTube 1      between users and a network between videos connected by
video-sharing website is one of the most recent and aston-        edges that represent being part of a same user’s favorite list.
ishing such examples of a Web phenomenon. Founded in
∗Data set will be made available in the camera ready version      This paper is organized as follows. On Section 2 we present
1                                                                 work on similar networks and virtual communities. We
    http://www.youtube.com
                                                                  present some background on the YouTube video-sharing com-
                                                                  munity in Section 3. Sections 4 and 5 detail the crawling
                                                                  process and tool, as well as the data sample we used, re-
                                                                  spectively. Our analysis of attributes and relationships is
                                                                  discussed in Section 6. Finally, we present our conclusions
                                                                  in Section 7.

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Characterizing the YouTube video-sharing community
2.   RELATED WORK                                                 compares structural properties of the co-authorship networks
The analysis of structural properties of large networks have      in publication databases from different areas, including biomed-
received much attention in the late years. Typically, stud-       ical research, physics, and computer science. He presents re-
ies include network properties such as degree distribution,       sults on the mean and distribution of co-authorship degrees
diameter, clustering coefficient, betweenness centrality, net-    and clustering coefficients for these networks and shows the
work resilience, mixing patterns, degree correlations, com-       presence of the small world effect in all of them. Kumar
munity structure, network navigation, etc. In this section,       et al. [5] characterize the profile of more than one million
we briefly outline some publications on the analysis of large-    LiveJournal users with regards to three main dimensions:
scale virtual communities, organized as social networks and       age, geography, and interests. They show how over 70%
information networks [11].                                        of friendship links among these users can be explained by
                                                                  combining these three dimensions. They also investigate the
Anh et al. [1] compare structural properties of sampled friend-   cultural aspect of highly-dynamic local, informal community
ship networks from two social networking services (SNSs),         formation in the blogospace, through the establishment of
namely MySpace 3 and Orkut 4 , and the entire topology of         short-lived reading, posting, and listing relationships among
the Cyworld 5 SNS. They uncover a two-period scaling be-          small groups of users.
havior in Cyworld’s degree distribution, being the exponent
of each period correspondent to the exponent of the degree        3. THE YOUTUBE VIDEO-SHARING COM-
distribution of MySpace and orkut, respectively. Also, they
show how Cyworld’s testimonial network (a subset of its              MUNITY
friendship network) presents a similar degree correlation to      The YouTube video-sharing community can be seen as an
real-life social networks. Friendship network properties are      heterogeneous graph with basically two 10 types of node:
also studied by Kumar et al. [6]. They present measure-           user and video.
ments on two Yahoo! SNSs: Flickr 6 , a one-million-node
photo-sharing community, and Yahoo! 360 7 , a five-million-       Users can upload, view, and share video clips. Videos can
node social networking website. They present a model of           be rated, and the average rating and the number of times
network growth by classifying users in these networks as          a video has been watched are both published. Unregistered
either (1) passive, loner members; (2) inviters, who bring        users can watch most videos on the site; registered users
offline friends to form isolated communities; and (3) linkers,    have the ability to upload an unlimited number of videos.
who play the role of bridging a large fraction of the entire      Related videos, determined by the title and tags, appear to
networking the network evolution.                                 the right of the video. In the site’s second year new functions
                                                                  were added, providing the ability to post video ‘responses’
Liben-Nowell et al. [8] study the formation of friendship links   and subscribe to content feeds for a particular user or users.
in the LiveJournal 8 blogging community. They show that,          YouTube had (and still has) a lot of traffic coming to the
among the nearly 500,000 LiveJournal users with mappable          site to view videos, but far fewer users actually creating and
geographic locations (at the level of towns and cities), the      posting content [15].
probability of two people being friends is inversely propor-
tional to the number of people geographically close to them.      Among all the potential relationships present in the YouTube
Also, they find that this property influences the formation       community, we consider the following in this paper:
of two thirds of the friendship links among these users and
prove analytically that short paths can be discovered in ev-
ery network in which it is present. Link formation is also             • user-user friendship: two users mutually regard each
investigated by Backstrom et al. [2]. They study the influ-              other as a friend;
ence of network structural properties on the establishment
of community membership links in two large sources of data:            • user-user subscription: a user subscribes to video feeds
the LiveJournal social networking and blogging service, with             from another user;
several million members and explicitly defined membership
                                                                       • user-video favoring: a user adds a video to his/her list
links, and DBLP 9 , a publication database with several hun-
                                                                         of favorites;
dred thousand authors with conferences regarded as proxies
for communities. They show that the tendency of individu-              • video-video relatedness: a video is regarded related to
als to join a community is influenced by both the number of              another one by the YouTube’s search engine.
friends they have within the community and how connected
these friends are to one another.
                                                                  4. CRAWLING YOUTUBE
Some works on information networks analysis have also em-         Due to the amount of data required to analyze YouTube,
ployed data sets from virtual communities. Newman [10]            using a tool like a web crawler to collect data is a necessity.
                                                                  A web crawler needs to visit web pages of videos and user
3                                                                 profiles. It must be able to follow links representing rela-
  http://www.myspace.com                                          tionships, like user friendship or commenting, and store the
4
  http://www.orkut.com                                            information on visited nodes and followed edges in a format
5
  http://www.cyworld.com                                          which can be further analyzed. As there is necessity for a
6
  http://www.flickr.com
7                                                                 large amount of data, the tool must be efficient and scalable.
  http://360.yahoo.com
8                                                                 10
  http://www.livejournal.com                                       YouTube also features collective entities, namely groups
9
  http://www.informatik.uni-trier.de/∼ ley/db/                    and contests, but they will not be considered in this work.
Characterizing the YouTube video-sharing community
In order to crawl YouTube, we have used our own tool. This         fourth 98,428 nodes (1,799 users and 96,629 videos) and in
tool is not only a crawler, but also an extractor, which gen-      the last 236,003 nodes (10,996 users and 225,007 videos). As
erates a graph representation of the network. It is model-         expected, the number of nodes in each layer grows exponen-
driven, in a way that it reads a network model file, contain-      tially.
ing HTML patterns of the network to be crawled. By creat-
ing a network model for YouTube and setting up a crawling          Our sample has a total of 338,001 nodes (12,832 users and
structure, we were able to achieve collection of large por-        325,179 videos) and indexed 12,131,796 nodes (625,383 users
tions of YouTube. Our crawler and extractor is presented in        and 11,506,413 videos). This means that through the 300
detail in [13].                                                    thousand nodes collected, more than 12 million other nodes
                                                                   were found by the relationships modeled.
Our tool uses the snowball sampling method [7]. This can
be done with a single seed node, or multiple seeds. For this       6. ANALYSIS OF YOUTUBE
work, we’ve used the single-seed approach. In single seed          The crawling process resulted in a dump file filled with
snowball sampling, we first choose a single node and all the       a graph representation of about more than three hundred
nodes directly linked to it are picked. Then all the nodes         nodes collected. From this data, several information can
connected to those picked in the last step are selected, and       be extracted and our objective is to analyze the impact of
this process is continued until the desired number of nodes        real-world relations in a technological environment.
is sampled. To control the number of nodes in the sampled
network, a necessary number of nodes is randomly chosen            The data can be split in two kinds: attributes and edges.
from the last layer. This is similar to a breadth-first crawling   Even though our collect sample is just a fraction of the en-
process [9].                                                       tire network, attributes are relative to whole network prop-
                                                                   erties since they are derived from data provided by YouTube
We’ve made more than one crawl, using different network            database. Differently, the edges compose a network with
model files. The purpose of this was to collect different          only the collected nodes. However, as the crawling process
views of the network, considering determined relationships         followed the snowball method, these partial networks reflect
at each crawl. Our crawling structure consisted of a sin-          properties of the whole network.
gle Pentium 4 3.2GHz server with 2 GBytes of RAM, and
6 client machines, with similar processors but 1 GByte of          6.1 Attributes of Nodes
RAM (our tool uses a client-server model).
                                                                   As depicted earlier, the two types of nodes considered are
                                                                   users and videos, each of them containing attributes useful
5.   DATA SAMPLE                                                   for our analysis. There are attributes that provide informa-
An important issue in any analysis of a collected network is       tion about network properties which are related to human
the validation of the gathered sample. The YouTube net-            interaction, such as channel views, number of subscribers
work is composed of millions of nodes and the task of collect-     and number of videos watched, from users, and number of
ing all of them is extremely hard. Therefore, only a part of       times favorited, number of views and number of comments,
the network is actually collected. For this reason, it is fun-     from videos.
damental that the fraction crawled represents the behavior
of the whole network.                                              The distribution of these attributes as well of the degrees
                                                                   of formed networks were all plotted in a log-log scale graph
There are several studies about sampling methods which             in order to identify the presence (or absence) of power-law
guarantee that a small collected fraction of the network rep-      distributions. Several works reported power-law degrees dis-
resents its entire behavior. The snowball sampling method [7]      tributions and how they are related to some real-word prop-
is a well-known method that reliably collects a part of a net-     erties. Power-laws are distributions where few values have
work that reflects the behavior of the whole network. The          high frequency and plenty of values have low frequencies
method start with a single seed node, and follows the re-          while still being a substantial part of the distribution (have-
lationships to discover new nodes in a breadth-first search        tail phenomenon). When observed over social relationships,
fashion. Even though there are some studies that mention           these distributions often imply on a “preferential attach-
the snowball method with multiple seeds, in this work we           ment” scenario, where nodes on the network tend to attach
used the single seed version since it is more diffused and         to certain more “popular” other nodes.
acknowledged.
                                                                   Figures 1, 2 and 3, show general statistics as well as dis-
For the crawling process we utilized the notions of nodes          tributions of attributes from users and videos, respectively.
and their relationships. A node is an user or a video of           On the general statistics we can observe that users ages and
the YouTube network, and the relationships are friendship,         videos duration distributions can be modeled as normal dis-
favoring, subscription and publication, from the users part,       tributions. Users nationality has a distribution with U.S.
and relatedness and ownership, from the videos part.               being by far the most frequent, while there is a heavy tail
                                                                   composed mostly of European countries. Videos categories
The gathered nodes can be grouped in layers where nodes            is a more balanced distribution, having entertainment, com-
belong to the same layer if they are distant (in the crawling      edy and music as most popular, maintaining a coherency
process) from the seed by the same number of hops. By this         with user ages distribution (majority of users formed by
definition, in the zero layer we have the seed (which was a        young people between 17 and 26 years). Distributions of
video), in the first layer 37 nodes (1 user and 36 videos),        attributes from users and videos follow power-law distribu-
in the third 3,546 nodes (36 users and 3,510 videos), in the       tions, and are discussed on the following paragraphs.
Characterizing the YouTube video-sharing community
100000
                                                                                      X=k
                                                                                     X>=k

                                      10000

                                       1000

                         Frequency
                                       100

    (a) Users age                       10

                                         1
                                              1      10    100   1000 10000 100000 1e+06 1e+07 1e+08
                                                                  Number of Views
                                                  (a) Number of Videos Watched
                                     100000
                                                                                      X=k
                                                                                     X>=k

                                      10000

                                       1000
                         Frequency

(b) Users Nationality
                                       100

                                        10

                                         1
                                              1      10    100   1000 10000 100000 1e+06 1e+07 1e+08
                                                                  Number of Views
                                                          (b) Channel Views
                                     100000
                                                                                      X=k
                                                                                     X>=k

                                      10000
(c) Videos Categories

                                       1000
                         Frequency

                                        100

                                        10

                                         1
                                              1      10    100   1000 10000 100000 1e+06 1e+07 1e+08
                                                                 Number of Subscribers
                                                    (c) Number of Subscribers
(d) Videos Duration
                        Figure 2: User Network Attributes Distribution
Figure 1: Statistics
Videos watched
                                                                                 The number of videos watched by a user indicate the utiliza-
                                                                                 tion of the YouTube service by him/her. As it can be seen in
                                                                                 Figure 2(a), most of the users have watched a small number
                                                                                 of videos. However, there are a few users that intensively
                                                                                 use the YouTube service, characterizing the distribution of
                                                                                 videos watched as power-law. This attribute accounts only
              1e+06
                                                                X=k              for views of logged users (a lot of viewers do not even have
                                                               X>=k
                                                                                 a user account).
              100000

               10000                                                             Number of subscribers
                                                                                 Users’ reputation is strongly connected to the videos they
  Frequency

                1000                                                             publish. A metric than can quantify this reputation is the
                                                                                 number of subscribers an user has. When someone subscribe
                100                                                              to a user, he asks to be notified every time a new video is
                                                                                 published by this user. The distribution of this attribute is
                 10                                                              shown in Figure 2(c).

                  1
                       1      10   100     1000 10000 100000 1e+06 1e+07 1e+08   Channel views
                                           Number of Comments                    Channel views is also connected to users’ reputation but this
                             (a) Number of Comments                              connection is weaker than that of the number of subscribers.
              1e+06
                                                                                 This is due to the fact that a user can be popular for a period
                                                                X=k
                                                               X>=k
                                                                                 of time and have a large number of channel views but, later,
              100000
                                                                                 lose his reputation and he still remain with a high value of
                                                                                 channel views. Figure 2(b) shows the distribution of the
               10000
                                                                                 users’ channel views.
  Frequency

               1000                                                              Videos views
                                                                                 One important characteristic of a video is its popularity.
                100                                                              Videos views indicate the video all-time popularity, since
                                                                                 its does not take into account when the views took place.
                 10                                                              As shown in Figure 3(b), the distribution has a “normal
                                                                                 like” distribution up to around videos with 50 visualizations.
                  1
                       1      10   100     1000 10000 100000 1e+06 1e+07 1e+08
                                                                                 For more than that, the distribution assumes a heavy tailed
                                            Number of Views                      power-law distribution behavior.
                               (b) Number of Views
              1e+06                                                              Users comments
                                                                X=k
                                                               X>=k              The number of users comments on a video is related to how
              100000                                                             controversial it is. As more polemic a video is, more users
                                                                                 will post their comments and discuss about the video’s con-
              10000                                                              tent. The distribution of the number of users comments can
                                                                                 be seen at Figure 3(a).
  Frequency

               1000

                                                                                 Number of times favorited
                100
                                                                                 A stronger (compared to video views) metric of popularity is
                                                                                 the number of users that included the video in their favorites
                 10
                                                                                 list. This attribute is more suitable because it reflects the
                                                                                 current status of the video and not an old popularity. Fur-
                  1
                       1      10   100     1000 10000 100000 1e+06 1e+07 1e+08   thermore, adding a video to a favorites list not only tells us
                                         Number of Times Favorited               that an user watched the video but it also reflects that he en-
                           (c) Number of Times Favorited                         joyed it. Figure 3(c) shows the distribution of this attribute
                                                                                 on the YouTube network.

Figure 3: Videos Network Attributes Distribution
                                                                                 6.2 Relationships
                                                                                 It is important to analyze the impact of human interac-
                                                                                 tion in a technological environment. In the YouTube com-
                                                                                 munity there are two major ways of users relate to each
                                                                                 other: through friendship and subscription. Both relation-
                                                                                 ships were extracted from collected data and had the result-
                                                                                 ing network analyzed. These networks were studied by the
10000
                                                                  X=k
                                                                 X>=k

                1000
    Frequency

                 100

                  10

                   1
                        1        10    100   1000 10000 100000 1e+06 1e+07 1e+08
                                              Number of Friends
                                        (a) Friendship                                           1e+06
                                                                                                                                                X=k
                100000                                                                                                                         X>=k
                                                                  X=k                            100000
                                                                 X>=k

                 10000                                                                           10000

                                                                                     Frequency
                  1000                                                                            1000
    Frequency

                                                                                                   100
                   100

                                                                                                    10
                    10
                                                                                                     1
                                                                                                          1   10   100     1000 10000 100000 1e+06 1e+07 1e+08
                        1                                                                                                Number of Related Videos
                            1     10   100   1000 10000 100000 1e+06 1e+07 1e+08
                                                                                                                   (a) Relatedness
                                               Number of Users
                                (b) In-degree in Subscription                                    1e+06
                                                                                                                                                X=k
                                                                                                                                               X>=k
                                                                                                 100000
  Figure 4: Degree Distribution of User Networks
                                                                                                 10000
                                                                                     Frequency

analysis of the degree distribution, number of nodes, clus-
                                                                                                  1000
tering coefficient (number of triangles in the first neighbor-
hood), longest shortest-path (L1 ) and average shortest-path
                                                                                                   100
(L2 ).
                                                                                                    10
We also identify two relationships between videos. The first
is relatedness, which relates similar videos through the use
                                                                                                     1
of tags and keywords. Although this is a relationship gen-                                                1   10   100     1000 10000 100000 1e+06 1e+07 1e+08
erated by a technological machine (YouTube generates re-                                                                 Number of Times Favorited
lated lists automatically), it is influenced by social relations,                                             (b) In-degree in Favoring
since a video can have a variable number of related videos,
depending on its associated keywords. For instance, a video
tagged as a soccer video, a very popular category, is likely to                    Figure 5: Degree Distribution of Videos Network
have many related videos. The second relationship between
videos is favorite lists. Since users can add videos to their
favorite lists, we can form a network of videos and connect
each two that are present on a same favorite list. This allows
us to identify clusters of videos and detect relatedness in a
different fashion than the first relation.

Figures 4 and 5 show degree distribution of users and videos
relationships, respectively. The following paragraphs further
detail these relationships.

Friendship
Through data collected from users nodes, it was possible to
create a representation of a graph were vertices are users
Network         #Nodes         CC       L1       L2               Relatedness
 Friendship       9,963      0.264221    10    2.76779             YouTube provides a way of finding videos related to each
 Subscription     8,575      0.176046    10    3.03550             other. This relationship defines a relatedness network where
                                                                   an edge exists between two videos if they are related by this
             Table 1: Networks Properties                          engine. This data was collect from links in the main page of
                                                                   videos.

and edges between them are created when they are friend            Although the search engine utilized to find related videos
to each other (in the YouTube context). Therefore, this            makes use of tags previous specified by human being users,
network represents how users are related one to another and        the mechanism that defines this relationship it is not based
the degree distribution characterizes popularity of users in       on human interaction. Therefore, the edges are defined
the YouTube community.                                             based on the the recall of an algorithm.

Figure 4(a) shows a scatter of the degrees distribution of the     The degree distribution of the relatedness network can be
friendship network. The single distribution behaves a little       seen in Figure 5(a). Probably because of the algorithmic
erratic because some nodes have odd degrees even though            source of the relationships, the distribution does not behaves
the friendship relationship in YouTube is reciprocal (degrees      properly as a power-law, it does only in some parts of the
should all be even because they are the sum of in and out          distribution.
degrees). The odd degrees happen because users accounts
can be suspended. When the crawler tries to collect these          Favoring
suspended users, it gather only the user identification and        Another kind of relationship present on the YouTube net-
stores on the dump, hence, the suspended user’s friendship         work is the one formed by the action when a user adds a
list is not collected which results in an odd degree of the        video to his/her favorites list. This list is extracted from
friend user. However, the cumulative distribution diminish         the user profile and its composed by a list of video identi-
the impact of these users and behaves like a power-law dis-        fiers.
tribution.
                                                                   Through this relationship we can build a bipartite graph,
In Table 1, some of the friendship network properties are          where every edge connects a user and a video. One interest-
listed. A network that has a high clustering coefficient (CC)      ing network that can be formed from the favoring network
and a small diameter is called a Small-World network. This         is the one formed with nodes of videos, and edges between
kind of network seen to emerge from a lot of different human       them if they appear in the favorites list of a same user. A
interactions and is well-known to be easily navigable and          comparison between this co-favoring video network and the
have a dense local cluster. Small-World networks merge             relatedness network could be used to analyze the effective-
two desirable properties of two famous type of graphs, small       ness of the YouTube related search engine.
diameter from random graphs and high clustering coefficient
from regular graphs (lattices).                                    Different from the other networks, the distribution that is
                                                                   more relevant in this network is the distribution of the in-
                                                                   degrees. This distribution is plotted on Figure 5(b) and
Subscription                                                       evidences a power-law distribution. This power-law differs
The subscription network was built upon the data collected         from the one found by the analysis of the attribute “number
from users nodes. From each user was gathered the list of          of times favorited”. This is due to the same reasons that
users whose new added videos should be notified to the col-        subscribers distribution differed from each other (as men-
lected user. This is an important network since it describes       tioned before). But the inclination of the distributions are
how users are interested in the content publishing of other        close one to another.
users. Nodes in this network with high degree are authori-
ties that will have their published videos watched by a large      7. CONCLUSIONS
public.                                                            In this work we were able to present a characterization of the
                                                                   YouTube video-sharing virtual community. We were able to
The in-degree distribution of this network represents the          collect a sample of this network using our own crawler and
distribution of the number of subscribers among YouTube            make different analysis over this data.
users. This distribution is on Figure 4(b) and it behaves
like a power-law distribution. The difference between this         By analyzing attributes and relationships we could see how
power-law and the one found on Figure 2(c) is due to the           this technological network has a distribution of content ex-
fact that both information are incomplete. Despite the fact        tremely influenced by social relationships. Visualizations
that the attributes are related to the whole network, our          of videos, relations among users and others have statistical
sample has not all the nodes to plot the real distribution. As     distributions that follow power-law functions, showing ev-
the built networks consider only the edges between collected       idence of Small-World models and preferential attachment
nodes, they are a fraction of the whole YouTube community.         scenarios.

The Table 1 shows some properties of the subscription net-         As we do not have knowledge of any other work with the
work. As it can be seen, it has a high clustering coefficient as   YouTube network, we present a first step towards charac-
well as a small diameter, which are properties of Small-world      terizing this important virtual community. Our results con-
networks.                                                          firm that YouTube is, as some social networks like Orkut
and MySpace, a technological network whose topology and            Wisdom Shapes Business, Economies, Societies and
connections are heavily influenced by human social behav-          Nations. Doubleday, May 2004.
ior.                                                          [15] Wikipedia. YouTube, May 2007. Available:
                                                                   http://en.wikipedia.org/wiki/YouTube.
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