YouTube is a video sharing website where users can upload, view, and comment on videos. The document discusses how YouTube was launched in 2005 and grew rapidly, serving over 100 million videos per day by 2006. A web crawler was used to collect data from YouTube, including over 300 user profiles and relationships between users like friendships and subscriptions. The collected data was analyzed to understand how human social interactions shape the technological networks on YouTube, with distributions of content and relationships following power-law functions consistent with small-world network models.

1. 
SOCIAL
M
EDIA- YOU
TUBE

2. 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”.

3. 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

4. INTRODUCTION
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.
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.

5. 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

6. 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

7. 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.

8. 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.

9. 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.

10. 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.

11. 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.

12. 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.

13. 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.