YouTube timeline

Loading...

来自 Princeton University 的课程

Networks Illustrated: Principles without Calculus

43 个评分

Princeton University

Networks Illustrated: Principles without Calculus

43 个评分

What makes WiFi faster at home than at a coffee shop? How does Google order its search results from the trillions of webpages on the Internet? Why does Verizon charge $15 for every GB of data we use? Is it really true that we are connected in six social steps or less? These are just a few of the many intriguing questions we can ask about the social and technical networks that form integral parts of our daily lives. This course is about exploring the answers, using a language that anyone can understand. We will focus on fundamental principles like “sharing is hard”, “crowds are wise”, and “network of networks” that have guided the design and sustainability of today’s networks, and summarize the theories behind everything from the social connections we make on platforms like Facebook to the technology upon which these websites run. Unlike other networking courses, the mathematics included here are no more complicated than adding and multiplying numbers. While mathematical details are necessary to fully specify the algorithms and systems we investigate, they are not required to understand the main ideas. We use illustrations, analogies, and anecdotes about networks as pedagogical tools in lieu of detailed equations. All the features of this course are available for free. It does not offer a certificate upon completion.

从本节课中

Viral Videos on YouTube

What does it take for a video to become "viral" on YouTube? In this lesson, we will take a look at some of the key factors and models that have been used to explain this phenomenon. At the core is the notion of information cascade in a network, which is the counterpart to the wisdom of crowds.

YouTube timeline4:50

Viral style and video recommendation7:36

Defining "viral"3:18

Information cascade & sequential decision making8:24

Number-Guessing Thought-Experiment3:11

First, second, and third "guessers"7:36

Analyzing cascades: Part I13:21

Analyzing Cascades: Part II10:25

Emperor's New Clothes4:34

Considerations5:14

与讲师见面

Christopher Brinton
Lecturer
Electrical Engineering
Mung Chiang
Professor
Electrical Engineering

Hello and welcome back to Networks Illustrated: Principle without Calculus.

In this lecture, we're going to look at the question,

why do certain videos go viral on You Tube?

Thinking back to the lectures on Amazon and

Netflix, we looked at the concept wisdom of crowds.

And we saw that under certain

circumstances, we could aggregate information coming from

a mass of people and come up with a good estimate for some value.

The key underlined assumption that was made in the wisdom

of crowds, was that people's opinions were independent of one another.

So we had independence.

What we're going to look at in this and the next

lecture, are situations where they're not independent of one another.

And rather, everybody can see the public actions of

the crowd and hence we have dependence of opinions.

A good example of this is when your looking to see whether or not

to watch a YouTube video based upon the total view count that video has.

And under certain circumstances as well,

depending upon the specific situation, we could

see how this dependence of information can

trigger what's known as an information cascade.

[SOUND] Where nobody is looking at their private information

anymore, and instead they just following suit with the

actions of the public and essentially ignoring their own gut

feelings, and hence we're going to see the exact opposite effect

of the wisdom of crowds, instead the fallacy of crowds.

As a result, in this and the next lecture the overarching

principle and theme is going to be that crowds are not so wise.

In this space of user-generated content,

YouTube has become the dominant sharing site.

On the site, we can find videos ranging from lectures on

academic subjects, to the music

of self-proclaimed quote unquote YouTube artists.

More recently, the site has also started a movie rental

and purchase business to compete against iTunes and the Studios.

So in this lecture, we want to explore the concept of viralization.

And in doing so, we're going to recognize how YouTube

is a perfect example of dependencies created by information spread.

But first, let's take a brief look at the timeline behind YouTube's rapid growth.

YouTube was founded in February 2005 at

the hands of three former PayPal employees.

From it's inception, the site grew at a rapid pace.

For instance, by July 2006, it had 65,000 videos

and about 100 million views it was getting daily.

And just a few months later in November

06, it was acquired by Google for 1.65 billion.

And of course the increase in YouTube view counts kept going.

For instance by October 2009, it broke 1 billion daily views.

This graph right here shows the views per day in billions.

So in October 2009 we had it breaking one, one billion views.

May 2010 it broke, two billion.

By, what, next year May 2011 it had gone up to three billion.

And January 2012, it hit the 4 billion mark.

An interesting thing to note here is that, the amount

of people that are watching the site, or the amount

of people that are on the site, is going to influence

the amount of content that you have on the site.

because as more people are going on the site, more

of them are going to be incentivized probably to make content.

And the more content you have on the site is going to in turn influence the

amount of people on the site which is

going to influence the amount of content and so forth.

And what's known as a positive feedback with them.

We'll look at this more in this lecture as opposed

to negative feed back loops that we, looked at before.

So by winter 2013, YouTube had over 1 billion

unique monthly viewers, with over 4 billion views per day.

Further, there is roughly 72 hours of content uploaded each minute.

So every minute it gets 72 hours worth of watching time.

Which means that in 1 day there is about 12 years of content.

So, if you think about that, what that's implying is

that, each day, YouTube gets enough content that you'd have to

take 12 years of your life to watch all the

content that was uploaded in that day, which is pretty remarkable.

YouTube's also the second largest search engine that

we wouldn't normally think of it as a,

as a search engine, it means that, the

second largest number of queries are entered into YouTube.

And that's of course, behind Google itself as the largest.

And Alexis ranked YouTube as the third

most visited website behind Google, which is

the first most visited website, and Facebook

which is the second most visited website.

So from all of this and seeing how popular YouTube has become, it's really become

somewhat of a viral phenomenon on itself,

and we'll turn our attention to viralization next.

探索我们的目录

免费加入并获得个性化推荐、更新和优惠。

Coursera 致力于普及全世界最好的教育，它与全球一流大学和机构合作提供在线课程。

© 2019 Coursera Inc. 保留所有权利。

通过 App Store 下载

通过 Google Play 获取