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 3270-90 Brazil {rodrygo,bpontes,rezende,loureiro}@dcc.ufmg.br ABSTRACT The YouTube video-sharing community is a recent and successful phenomenon that provides an expressive representation of a social network. Despite its accelerated growth, a deep study of YouTube s topology has not yet been made available. For this work, we have collected a representative sample of YouTube using our Crawlanga tool and analyzed both its structural properties, as well as its social relationships among users, among videos, and between users and videos. We analyze properties such as profile of users and popularity of videos in order to highlight the impact of social relationships on a content-sharing network. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications- Data Mining; J.4 [Computer Applications]: Social and behavioral sciences General Terms Human factors, Measurement Keywords Virtual communities, network sampling, network analysis. INTRODUCTION The last decade has witnessed the emergence of several popularity phenomena through the word-of-mouth and self-publishing made feasible by the World Wide Web. This is true for people, the content they produce, and the vehicles that distribute their production. Some of these phenomena have declined or have been replaced as rapid as they rose, while others have retained a steady pace of growth. The TIME s Invention of the Year for 2006 [4], the YouTube video-sharing website is one of the most recent and astonishing such examples of a Web phenomenon. Founded in Data set will be made available in the camera ready version http://www.youtube.com February 2005, YouTube was officially launched in December of the same year and has not stopped growing since then. By July 2006, the site reported to serve 0 million videos per day, with a daily upload of more than 65,000 videos and nearly 20 million unique visitors per month a 29% share of the US multimedia entertainment market and 60% of all videos watched online [2]. Its storage demands were estimated at around 45 terabytes with several million dollar expenses on bandwidth per month [3]. Within one year of its launch, YouTube was purchased by Google for US$.65 billion in stock. YouTube s success can be seen as an example of the wisdom of crowds [4]: the site exerts no control over its users freedom for publishing 2, in such a way that users not only share their videos with a few friends, but instead participate in a huge decentralized community by creating and consuming terabytes of video content, ranging from home-made standup performances to eyewitness footages from inside news as they occur anywhere in the world. Despite its enormous popularity and the sums of money involved, it is rather surprising that (at least to our knowledge) no study has been carried on unveiling the virtual community behind YouTube. In this paper, we present an analysis of YouTube network, based on a sample of it we were able to collect using a crawler tool. In our analysis we focus users and videos, and attributes and relationships between them. We observe attributes such as number of videos visualizations, users subscription, users favorite lists, commenting, and others. We also model the collected network as different networks including specific views as, for instance, a friendship network between users and a network between videos connected by edges that represent being part of a same user s favorite list. This paper is organized as follows. On Section 2 we present work on similar networks and virtual communities. We present some background on the YouTube video-sharing community in Section 3. Sections 4 and 5 detail the crawling process and tool, as well as the data sample we used, respectively. Our analysis of attributes and relationships is discussed in Section 6. Finally, we present our conclusions in Section 7. 2 According to the site policy, copyrighted or inappropriate content is reviewed after being flagged by the community.
2. RELATED WORK The analysis of structural properties of large networks have received much attention in the late years. Typically, studies include network properties such as degree distribution, diameter, clustering coefficient, betweenness centrality, network resilience, mixing patterns, degree correlations, community structure, network navigation, etc. In this section, we briefly outline some publications on the analysis of largescale virtual communities, organized as social networks and information networks []. Anh et al. [] compare structural properties of sampled friendship networks from two social networking services (SNSs), namely MySpace 3 and Orkut 4, and the entire topology of the Cyworld 5 SNS. They uncover a two-period scaling behavior in Cyworld s degree distribution, being the exponent of each period correspondent to the exponent of the degree distribution of MySpace and orkut, respectively. Also, they show how Cyworld s testimonial network (a subset of its friendship network) presents a similar degree correlation to real-life social networks. Friendship network properties are also studied by Kumar et al. [6]. They present measurements on two Yahoo! SNSs: Flickr 6, a one-million-node photo-sharing community, and Yahoo! 360 7, a five-millionnode social networking website. They present a model of network growth by classifying users in these networks as either () passive, loner members; (2) inviters, who bring offline friends to form isolated communities; and (3) linkers, who play the role of bridging a large fraction of the entire networking the network evolution. Liben-Nowell et al. [8] study the formation of friendship links in the LiveJournal 8 blogging community. They show that, among the nearly 500,000 LiveJournal users with mappable geographic locations (at the level of towns and cities), the probability of two people being friends is inversely proportional to the number of people geographically close to them. Also, they find that this property influences the formation of two thirds of the friendship links among these users and prove analytically that short paths can be discovered in every network in which it is present. Link formation is also investigated by Backstrom et al. [2]. They study the influence of network structural properties on the establishment of community membership links in two large sources of data: the LiveJournal social networking and blogging service, with several million members and explicitly defined membership links, and DBLP 9, a publication database with several hundred thousand authors with conferences regarded as proxies for communities. They show that the tendency of individuals to join a community is influenced by both the number of friends they have within the community and how connected these friends are to one another. Some works on information networks analysis have also employed data sets from virtual communities. Newman [] 3 http://www.myspace.com 4 http://www.orkut.com 5 http://www.cyworld.com 6 http://www.flickr.com 7 http://360.yahoo.com 8 http://www.livejournal.com 9 http://www.informatik.uni-trier.de/ ley/db/ compares structural properties of the co-authorship networks in publication databases from different areas, including biomedical research, physics, and computer science. He presents results on the mean and distribution of co-authorship degrees and clustering coefficients for these networks and shows the presence of the small world effect in all of them. Kumar et al. [5] characterize the profile of more than one million LiveJournal users with regards to three main dimensions: age, geography, and interests. They show how over 70% of friendship links among these users can be explained by combining these three dimensions. They also investigate the cultural aspect of highly-dynamic local, informal community formation in the blogospace, through the establishment of short-lived reading, posting, and listing relationships among small groups of users. 3. THE YOUTUBE VIDEO-SHARING COM- MUNITY The YouTube video-sharing community can be seen as an heterogeneous graph with basically two types of node: user and video. Users can upload, view, and share video clips. Videos can be rated, and the average rating and the number of times a video has been watched are both published. Unregistered users can watch most videos on the site; registered users have the ability to upload an unlimited number of videos. Related videos, determined by the title and tags, appear to the right of the video. In the site s second year new functions were added, providing the ability to post video responses and subscribe to content feeds for a particular user or users. YouTube had (and still has) a lot of traffic coming to the site to view videos, but far fewer users actually creating and posting content [5]. Among all the potential relationships present in the YouTube community, we consider the following in this paper: user-user friendship: two users mutually regard each other as a friend; user-user subscription: a user subscribes to video feeds from another user; user-video favoring: a user adds a video to his/her list of favorites; video-video relatedness: a video is regarded related to another one by the YouTube s search engine. 4. CRAWLING YOUTUBE Due to the amount of data required to analyze YouTube, 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 profiles. It must be able to follow links representing relationships, like user friendship or commenting, and store the information on visited nodes and followed edges in a format which can be further analyzed. As there is necessity for a large amount of data, the tool must be efficient and scalable. YouTube also features collective entities, namely groups and contests, but they will not be considered in this work.
In order to crawl YouTube, we have used our own tool. This tool is not only a crawler, but also an extractor, which generates a graph representation of the network. It is modeldriven, in a way that it reads a network model file, containing HTML patterns of the network to be crawled. By creating a network model for YouTube and setting up a crawling structure, we were able to achieve collection of large portions of YouTube. Our crawler and extractor is presented in detail in [3]. Our tool uses the snowball sampling method [7]. This can be done with a single seed node, or multiple seeds. For this work, we ve used the single-seed approach. In single seed snowball sampling, we first choose a single node and all the nodes directly linked to it are picked. Then all the nodes connected to those picked in the last step are selected, and this process is continued until the desired number of nodes is sampled. To control the number of nodes in the sampled network, a necessary number of nodes is randomly chosen from the last layer. This is similar to a breadth-first crawling process [9]. We ve made more than one crawl, using different network model files. The purpose of this was to collect different views of the network, considering determined relationships at each crawl. Our crawling structure consisted of a single Pentium 4 3.2GHz server with 2 GBytes of RAM, and 6 client machines, with similar processors but GByte of RAM (our tool uses a client-server model). 5. DATA SAMPLE An important issue in any analysis of a collected network is the validation of the gathered sample. The YouTube network is composed of millions of nodes and the task of collecting all of them is extremely hard. Therefore, only a part of the network is actually collected. For this reason, it is fundamental that the fraction crawled represents the behavior of the whole network. There are several studies about sampling methods which guarantee that a small collected fraction of the network represents its entire behavior. The snowball sampling method [7] is a well-known method that reliably collects a part of a network that reflects the behavior of the whole network. The method start with a single seed node, and follows the relationships to discover new nodes in a breadth-first search fashion. Even though there are some studies that mention the snowball method with multiple seeds, in this work we used the single seed version since it is more diffused and acknowledged. For the crawling process we utilized the notions of nodes and their relationships. A node is an user or a video of the YouTube network, and the relationships are friendship, favoring, subscription and publication, from the users part, and relatedness and ownership, from the videos part. The gathered nodes can be grouped in layers where nodes belong to the same layer if they are distant (in the crawling process) from the seed by the same number of hops. By this definition, in the zero layer we have the seed (which was a video), in the first layer 37 nodes ( user and 36 videos), in the third 3,546 nodes (36 users and 3,5 videos), in the fourth 98,428 nodes (,799 users and 96,629 videos) and in the last 236,003 nodes (,996 users and 225,007 videos). As expected, the number of nodes in each layer grows exponentially. Our sample has a total of 338,00 nodes (2,832 users and 325,79 videos) and indexed 2,3,796 nodes (625,383 users and,506,43 videos). This means that through the 300 thousand nodes collected, more than 2 million other nodes were found by the relationships modeled. 6. ANALYSIS OF YOUTUBE The crawling process resulted in a dump file filled with a graph representation of about more than three hundred nodes collected. From this data, several information can be extracted and our objective is to analyze the impact of real-world relations in a technological environment. The data can be split in two kinds: attributes and edges. Even though our collect sample is just a fraction of the entire network, attributes are relative to whole network properties since they are derived from data provided by YouTube database. Differently, the edges compose a network with only the collected nodes. However, as the crawling process followed the snowball method, these partial networks reflect properties of the whole network. 6. Attributes of Nodes As depicted earlier, the two types of nodes considered are users and videos, each of them containing attributes useful for our analysis. There are attributes that provide information about network properties which are related to human interaction, such as channel views, number of subscribers and number of videos watched, from users, and number of times favorited, number of views and number of comments, from videos. The distribution of these attributes as well of the degrees of formed networks were all plotted in a log-log scale graph in order to identify the presence (or absence) of power-law distributions. Several works reported power-law degrees distributions and how they are related to some real-word properties. Power-laws are distributions where few values have high frequency and plenty of values have low frequencies while still being a substantial part of the distribution (havetail phenomenon). When observed over social relationships, these distributions often imply on a preferential attachment scenario, where nodes on the network tend to attach to certain more popular other nodes. Figures, 2 and 3, show general statistics as well as distributions of attributes from users and videos, respectively. On the general statistics we can observe that users ages and videos duration distributions can be modeled as normal distributions. Users nationality has a distribution with U.S. being by far the most frequent, while there is a heavy tail composed mostly of European countries. Videos categories is a more balanced distribution, having entertainment, comedy and music as most popular, maintaining a coherency with user ages distribution (majority of users formed by young people between 7 and 26 years). Distributions of attributes from users and videos follow power-law distributions, and are discussed on the following paragraphs.
0000 000 (a) Users age 00 0 0 00 000 0000 e+06 e+07 e+08 Number of Views (a) Number of Videos Watched 0000 (b) Users Nationality 000 00 0 0 00 000 0000 e+06 e+07 e+08 Number of Views (b) Channel Views 0000 000 (c) Videos Categories 00 0 0 00 000 0000 e+06 e+07 e+08 Number of Subscribers (c) Number of Subscribers (d) Videos Duration Figure 2: User Network Attributes Distribution Figure : Statistics
e+06 0000 Videos watched The number of videos watched by a user indicate the utilization 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 for views of logged users (a lot of viewers do not even have a user account). 000 00 0 Number of subscribers Users reputation is strongly connected to the videos they publish. A metric than can quantify this reputation is the number of subscribers an user has. When someone subscribe to a user, he asks to be notified every time a new video is published by this user. The distribution of this attribute is shown in Figure 2(c). e+06 0000 000 0 00 000 0000 e+06 e+07 e+08 00 0 e+06 0000 000 00 Number of Comments (a) Number of Comments 0 00 000 0000 e+06 e+07 e+08 0 Number of Views (b) Number of Views 0 00 000 0000 e+06 e+07 e+08 Number of Times Favorited (c) Number of Times Favorited Channel views Channel views is also connected to users reputation but this connection is weaker than that of the number of subscribers. This is due to the fact that a user can be popular for a period of time and have a large number of channel views but, later, lose his reputation and he still remain with a high value of channel views. Figure 2(b) shows the distribution of the users channel views. Videos views One important characteristic of a video is its popularity. Videos views indicate the video all-time popularity, since its does not take into account when the views took place. As shown in Figure 3(b), the distribution has a normal like distribution up to around videos with 50 visualizations. For more than that, the distribution assumes a heavy tailed power-law distribution behavior. Users comments The number of users comments on a video is related to how controversial it is. As more polemic a video is, more users will post their comments and discuss about the video s content. The distribution of the number of users comments can be seen at Figure 3(a). Number of times favorited A stronger (compared to video views) metric of popularity is the number of users that included the video in their favorites list. This attribute is more suitable because it reflects the current status of the video and not an old popularity. Furthermore, adding a video to a favorites list not only tells us that an user watched the video but it also reflects that he enjoyed 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 interaction in a technological environment. In the YouTube community there are two major ways of users relate to each other: through friendship and subscription. Both relationships were extracted from collected data and had the resulting network analyzed. These networks were studied by the
000 00 0 0 00 000 0000 e+06 e+07 e+08 0000 000 Number of Friends (a) Friendship e+06 0000 000 00 0 00 0 0 00 000 0000 e+06 e+07 e+08 Number of Users (b) In-degree in Subscription Figure 4: Degree Distribution of User Networks analysis of the degree distribution, number of nodes, clustering coefficient (number of triangles in the first neighborhood), longest shortest-path (L ) and average shortest-path (L 2). e+06 0000 000 00 0 00 000 0000 e+06 e+07 e+08 0 Number of Related Videos (a) Relatedness We also identify two relationships between videos. The first is relatedness, which relates similar videos through the use of tags and keywords. Although this is a relationship generated by a technological machine (YouTube generates related lists automatically), it is influenced by social relations, 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 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. 0 00 000 0000 e+06 e+07 e+08 Number of Times Favorited (b) In-degree in Favoring Figure 5: Degree Distribution of Videos Network 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 L L 2 Friendship 9,963 0.26422 2.76779 Subscription 8,575 0.76046 3.03550 Table : Networks Properties and edges between them are created when they are friend to each other (in the YouTube context). Therefore, this network represents how users are related one to another and the degree distribution characterizes popularity of users in the YouTube community. Figure 4(a) shows a scatter of the degrees distribution of the friendship network. The single distribution behaves a little erratic because some nodes have odd degrees even though the friendship relationship in YouTube is reciprocal (degrees should all be even because they are the sum of in and out degrees). The odd degrees happen because users accounts can be suspended. When the crawler tries to collect these suspended users, it gather only the user identification and stores on the dump, hence, the suspended user s friendship list is not collected which results in an odd degree of the friend user. However, the cumulative distribution diminish the impact of these users and behaves like a power-law distribution. In Table, some of the friendship network properties are listed. A network that has a high clustering coefficient (CC) and a small diameter is called a Small-World network. This kind of network seen to emerge from a lot of different human interactions and is well-known to be easily navigable and have a dense local cluster. Small-World networks merge two desirable properties of two famous type of graphs, small diameter from random graphs and high clustering coefficient from regular graphs (lattices). Subscription The subscription network was built upon the data collected from users nodes. From each user was gathered the list of users whose new added videos should be notified to the collected user. This is an important network since it describes how users are interested in the content publishing of other users. Nodes in this network with high degree are authorities that will have their published videos watched by a large public. The in-degree distribution of this network represents the distribution of the number of subscribers among YouTube users. This distribution is on Figure 4(b) and it behaves like a power-law distribution. The difference between this power-law and the one found on Figure 2(c) is due to the fact that both information are incomplete. Despite the fact that the attributes are related to the whole network, our sample has not all the nodes to plot the real distribution. As the built networks consider only the edges between collected nodes, they are a fraction of the whole YouTube community. The Table shows some properties of the subscription network. As it can be seen, it has a high clustering coefficient as well as a small diameter, which are properties of Small-world networks. Relatedness YouTube provides a way of finding videos related to each other. This relationship defines a relatedness network where an edge exists between two videos if they are related by this engine. This data was collect from links in the main page of videos. Although the search engine utilized to find related videos makes use of tags previous specified by human being users, the mechanism that defines this relationship it is not based on human interaction. Therefore, the edges are defined based on the the recall of an algorithm. The degree distribution of the relatedness network can be seen in Figure 5(a). Probably because of the algorithmic source of the relationships, the distribution does not behaves properly as a power-law, it does only in some parts of the distribution. Favoring Another kind of relationship present on the YouTube network is the one formed by the action when a user adds a video to his/her favorites list. This list is extracted from the user profile and its composed by a list of video identifiers. Through this relationship we can build a bipartite graph, where every edge connects a user and a video. One interesting network that can be formed from the favoring network is the one formed with nodes of videos, and edges between them if they appear in the favorites list of a same user. A comparison between this co-favoring video network and the relatedness network could be used to analyze the effectiveness of the YouTube related search engine. Different from the other networks, the distribution that is more relevant in this network is the distribution of the indegrees. This distribution is plotted on Figure 5(b) and evidences a power-law distribution. This power-law differs from the one found by the analysis of the attribute number of times favorited. This is due to the same reasons that subscribers distribution differed from each other (as mentioned before). But the inclination of the distributions are close one to another. 7. CONCLUSIONS In this work we were able to present a characterization of the YouTube video-sharing virtual community. We were able to collect a sample of this network using our own crawler and make different analysis over this data. By analyzing attributes and relationships we could see how this technological network has a distribution of content extremely influenced by social relationships. Visualizations of videos, relations among users and others have statistical distributions that follow power-law functions, showing evidence of Small-World models and preferential attachment scenarios. As we do not have knowledge of any other work with the YouTube network, we present a first step towards characterizing this important virtual community. Our results confirm that YouTube is, as some social networks like Orkut
and MySpace, a technological network whose topology and connections are heavily influenced by human social behavior. 8. REFERENCES [] Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong. Analysis of topological characteristics of huge online social networking services. In WWW 07: Proceedings of the 6th international conference on World Wide Web, pages 835 844, Banff, Alberta, Canada, 2007. ACM Press. [2] L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In KDD 06: Proceedings of the 2th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 44 54, Philadelphia, PA, USA, 2006. ACM Press. [3] L. Gomes. Will all of us get our 5 minutes on a YouTube video? The Wall Street Journal, August 2006. Available: http://online.wsj.com/public/article/ SB568929868048904- f92aczytlctkrtsiz8vumr3ezci 20070830.html. [4] L. Grossman. TIME Best Inventions 2006. TIME, November 2006. Available: http://www.time.com/ time/2006/techguide/bestinventions. [5] R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. Structure and evolution of blogspace. Communications of the ACM, 47(2):35 39, 2004. [6] R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. In KDD 06: Proceedings of the 2th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 6 67, Philadelphia, PA, USA, 2006. ACM Press. [7] S. H. Lee, P.-J. Kim, and H. Jeong. Statistical properties of sampled networks. Physical Review E, 73:062, 2006. [8] D. Liben-Nowell, J. Novak, R. Kumar, P. Raghavan, and A. Tomkins. Geographic routing in social networks. Proceedings of the National Academy of Sciences, 2(33):623 628, 2005. [9] M. Najork and J. L. Wiener. Breadth-first crawling yields high-quality pages. In WWW 0: Proceedings of the th international conference on World Wide Web, pages 4 8, Hong Kong, Hong Kong, 200. ACM Press. [] M. E. J. Newman. The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98:404, 200. [] M. E. J. Newman. The structure and function of complex networks, 2003. [2] Reuters. YouTube serves up 0 million videos a day online. USA TODAY, July 2006. Available: http://www.usatoday.com/tech/news/ 2006-07-6-youtube-views x.htm. [3] B. P. S. Rocha, R. L. T. Santos, C. G. Rezende, A. A. F. Loureiro, and V. A. F. Almeida. Model-driven crawling and extraction of web-based virtual communities. Submitted, May 2007. [4] J. Surowiecki. The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. Doubleday, May 2004. [5] Wikipedia. YouTube, May 2007. Available: http://en.wikipedia.org/wiki/youtube.