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YOUTUBE

“YouTube is a “video-sharing website “on which users can upload, share, view and comment on videos”.

“YouTube is a “video-sharing website “on which users can upload, share, view and comment on videos”.

YOUTUBE * Provides a venue for sharing videos among friends and family as

well as a showcase for new and experienced videographers.

* YouTube has become a destination for ambitious videographers, as well as amateurs who want to make a statement.

* In the 2008 presidential campaign, videos of Barack Obama and John McCain were viewed more than two billion times according to media firm Tube Mogul.

* Reach beyond your website* Encourages sharing

* Provides a venue for sharing videos among friends and family as well as a showcase for new and experienced videographers.

* YouTube has become a destination for ambitious videographers, as well as amateurs who want to make a statement.

* In the 2008 presidential campaign, videos of Barack Obama and John McCain were viewed more than two billion times according to media firm Tube Mogul.

* Reach beyond your website* Encourages sharing

INTRODUCTIONThe TIME’s Invention of the Year for 2006 the YouTube video-sharing

website is one of the most recent and astonishing such examples of a Web phenomenon. Founded in 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 100 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.

The TIME’s Invention of the Year for 2006 the YouTube video-sharing website is one of the most recent and astonishing such examples of a Web phenomenon. Founded in 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 100 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.

Its storage demands were estimated at around 45 terabytes with several million dollar expenses on bandwidth per month . Within one year of its launch, YouTube was purchased by Google for US$1.65 billion in stock.

*YouTube’s success can be seen as an example of the “wisdom of crowds” 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 stand-up performances to eyewitness footages from inside news as they occur anywhere in the world.

Its storage demands were estimated at around 45 terabytes with several million dollar expenses on bandwidth per month . Within one year of its launch, YouTube was purchased by Google for US$1.65 billion in stock.

*YouTube’s success can be seen as an example of the “wisdom of crowds” 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 stand-up performances to eyewitness footages from inside news as they occur anywhere in the world.

INTRODUCTION

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.

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.

INTRODUCTION

THE YOUTUBE VIDEO-SHARING COMMUNITY

The YouTube video-sharing community can be seen as an heterogeneous graph with basically two 10 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.

The YouTube video-sharing community can be seen as an heterogeneous graph with basically two 10 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.

THE YOUTUBE VIDEO-SHARING COMMUNITY

*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.

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.

*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.

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.

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.

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.

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 is a well-known method that reliably collects a part of a network that reflects the behavior of the whole network. Even though there are some studies that mention the snowball method with multiple seeds.

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 is a well-known method that reliably collects a part of a network that reflects the behavior of the whole network. Even though there are some studies that mention the snowball method with multiple seeds.

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 the 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 the 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.

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 the 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 the 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.

RELATIONSHIP

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 analysis of the degree distribution, number of nodes, clustering coefficient, longest shortest and average shortest.

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 analysis of the degree distribution, number of nodes, clustering coefficient, longest shortest and average shortest.

CONCLUSIONS

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.

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.