Visualizing the world's Twitter data - Jer Thorp

Transcriber: Andrea McDonough Reviewer: Bedirhan Cinar

翻译人员: Zhimin Lin 校对人员: QI YU

A couple of years ago I started using Twitter,

几年前我开始用推特，

and one of the things that really charmed me about Twitter

它最吸引我的一点

is that people would wake up in the morning

是人们会在早上起床的时候

and they would say, "Good morning!"

发一条推特说：“早上好！”

which I thought,

我觉得这个……

I'm a Canadian,

我是个加拿大人，

so I was a little bit,

所以我有点，

I liked that politeness.

我喜欢这种礼貌。

And so, I'm also a giant nerd,

同时我也是个典型的技术男，

and so I wrote a computer program

于是我写了个

that would record 24 hours of everybody on Twitter

记录24小时里推特上 所有发了“早上好！”的人的程序。

saying, "Good morning!"

And then I asked myself my favorite question,

之后我问了自己一个我最喜欢的问题：

"What would that look like?"

“那会是个什么样子？”

Well, as it turns out, I think it would look something like this.

结果是，我想看起来像这样：

Right, so we'd see this wave of people

我们看到这个

saying, "Good morning!" across the world as they wake up.

由世界各地醒来时发“早上好！”的人所组成的人浪

Now the green people, these are people that wake up

这些绿色所代表的人

at around 8 o'clock in the morning,

在早上8点左右醒来。

Who wakes up at 8 o'clock or says, "Good morning!" at 8?

谁8点起床或者8点时发“早上好”？

And the orange people,

这些橙色代表的人，

they say, "Good morning!" around 9.

他们在9点左右发“早上好！”。

And the red people, they say, "Good morning!" around 10.

之后这些红色代表的人， 他们在10点左右发“早上好！”。

Yeah, more at 10's than, more at 10's than 8's.

是的，10点的人比8点的多。

And actually if you look at this map,

而且事实上如果你观察这幅图，

we can learn a little bit about how people wake up

我们就能稍微了解世界各地 人们起床时间的不同。

in different parts of the world.

People on the West Coast, for example,

比如说西海岸的人

they wake up a little bit later

就比东海岸的人起得稍晚一些。

than those people on the East Coast.

But that's not all that people say on Twitter, right?

但人们在推特上不只是发这个，对吧？

We also get these really important tweets, like,

我们也收到这些非常重要的推特，诸如

"I just landed in Orlando!! [plane sign, plane sign]"

“我刚刚在奥兰多降落了！！[飞机][飞机]”

Or, or, "I just landed in Texas [exclamation point]!"

或者，“我刚刚降落在德州（感叹号）”

Or "I just landed in Honduras!"

又或者，“我刚刚在洪都拉斯降落了！”

These lists, they go on and on and on,

这些内容没完没了，

all these people, right?

总有人在发这些，是吧？

So, on the outside, these people are just telling us

那么，表面上看，这些人只是告诉我们

something about how they're traveling.

一些他们如何旅行的信息。

But we know the truth, don't we?

但是我们知道真相，不是吗？

These people are show-offs!

这些人就是在炫耀！

They are showing off that they're in Cape Town and I'm not.

炫耀他们在开普敦而我不在！

So I thought, how can we take this vanity

于是我想，我们如何才能利用这些虚荣

and turn it into utility?

并将其转化成实用的东西？

So using a similar approach that I did with "Good morning,"

因此我用类似于处理“早上好”的方法

I mapped all those people's trips

将所有人的旅行制成统计图，

because I know where they're landing,

因为我知道他们在哪落地，

they just told me,

他们直接告诉我了，

and I know where they live

而且我知道他们在哪居住，

because they share that information on their Twitter profile.

因为他们推特的个人简介上都写着呢。

So what I'm able to do with 36 hours of Twitter

所以我得以在推特上花36个小时

is create a model of how people are traveling

来建立一个

关于世界各地的人在此期间如何旅行的模型。

around the world during that 36 hours.

And this is kind of a prototype

这是一种原始模型，

because I think if we listen to everybody

因为我认为如果我们留意推特和脸书

on Twitter and Facebook and the rest of our social media,

和其它一切社交媒体上的所有人，

we'd actually get a pretty clear picture

我们其实会获得一幅清晰反映

of how people are traveling from one place to the other,

人们如何在各地之间旅行的图像，

which is actually turns out to be a very useful thing for scientists,

而这幅图像事实上对科学家来说非常有用，

particularly those who are studying how disease is spread.

尤其是那些研究疾病扩散问题的专家。

So, I work upstairs in the New York Times,

我在《纽约时报》工作，

and for the last two years,

在过去两年里，

we've been working on a project called, "Cascade,"

我们一直做一个叫“Cascade”的项目，

which in some ways is kind of similar to this one.

它在某种程度上和这个模型很相似。

But instead of modeling how people move,

但是我们不是对人们如何流动

we're modeling how people talk.

而是对人们如何发表言论进行建模。

We're looking at what does a discussion look like.

我们在研究某一场讨论是看起来是怎样的。

Well, here's an example.

这里有一个例子

This is a discussion around an article called,

这是一场围绕一篇文章的讨论，

"The Island Where People Forget to Die".

《那个人们忘记死亡的小岛》。

It's about an island in Greece where people live

它描述了一个希腊的小岛，

a really, really, really, really, really, really long time.

岛上的人们都非常、非常、非常、非常、非常、非常长寿。

And what we're seeing here

这里我们所看到的

is we're seeing a conversation that's stemming

是一场从左下角那第一条推特开始 延伸开来的讨论。

from that first tweet down in the bottom, left-hand corner.

So we get to see the scope of this conversation

因此我们得以了解9小时里

over about 9 hours right now,

这场讨论的规模。

we're going to creep up to 12 hours here in a second.

我们来把时间跨度拉大到12小时。

But, we can also see what that conversation

我们也可以在三维的模式下

looks like in three dimensions.

观察这场讨论。

And that three-dimensional view is actually much more useful for us.

而且这种三维的视角其实对我们更加有用。

As humans, we are really used to things

因为作为人类，我们非常习惯于三维的事物。

that are structured as three dimensions.

So, we can look at those little off-shoots of conversation,

所以我们能够看着讨论的那些细小分支

we can find out what exactly happened.

来了解到底发生了什么。

And this is an interactive, exploratory tool

这是一个交互式的、探索式的工具，

so we can go through every step in the conversation.

所以我们可以仔细研究这个讨论的每一步。

We can look at who the people were,

我们可以看看这些都是什么人、

what they said,

他们说了什么、

how old they are,

他们年纪多大、

where they live,

他们住在哪里、

who follows them,

谁关注了他们，

and so on, and so on, and so on.

等等等等。

So, the Times creates about 6,500 pieces of content every month,

《纽约时报》每个月产生大约6500篇文章，

and we can model every single one

我们可以为每一篇所引发的议论

of the conversations that happen around them.

都建立一个模型。

And they look somewhat different.

它们看起来不太一样。

Depending on the story

取决于故事本身

and depending on how fast people are talking about it

以及它引起人们议论的速度

and how far the conversation spreads,

以及议论传播的范围，

these structures, which I call these conversational architectures,

这些结构，我管它们叫“讨论大楼”，

end up looking different.

最终看起来不尽相同。

So, these projects that I've shown you,

我向你们展示的这些项目

I think they all involve the same thing:

我认为它们在做同样的事情：

we can take small pieces of data

我们可以将碎片化的数据

and by putting them together,

拼凑起来，

we can generate more value,

我们可以产生更大的价值，

we can do more exciting things with them.

我们可以用它们来做更激动人心的事情。

But so far we've only talked about Twitter, right?

但是目前为止我们只提到了推特，对吧？

And Twitter isn't all the data.

而推特不是数据的全部。

We learned a moment ago

正如刚才讨论的

that there is tons and tons,

网络上有很多

tons more data out there.

很多很多数据。

And specifically, I want you to think about one type of data

我尤其要向你们介绍其中一种，

because all of you guys,

因为你们所有人，

everybody in this audience, we,

这里的每一位听众，我们，

we, me as well,

我们，包括我在内，

are data-making machines.

都是产生数据的机器。

We are producing data all the time.

我们时时刻刻都在产生数据。

Every single one of us, we're producing data.

我们每一个人，我们都在产生数据。

Somebody else, though, is storing that data.

也有一些人在储存这些数据。

Usually we put our trust into companies to store that data,

通常来说我们信任各种储存数据的公司，

but what I want to suggest here

但是我要在这里提出的是

is that rather than putting our trust

相比起信任那些公司，

in companies to store that data,

让它们储存数据，

we should put the trust in ourselves

我们应该相信我们自己

because we actually own that data.

因为我们拥有那些数据。

Right, that is something we should remember.

这是我们应该牢记的。

Everything that someone else measures about you,

他人对你的任何估量

you actually own.

都属于你。

So, it's my hope,

因此，我希望——

maybe because I'm a Canadian,

或许因为我是个加拿大人——

that all of us can come together

我们所有人能带着 我们储存的宝贵数据走到一起，

with this really valuable data that we've been storing,

and we can collectively launch that data

我们能一起利用那些数据

toward some of the world's most difficulty problems

来解决某些世界上最棘手的难题，

because big data can solve big problems,

因为大数据能解决大问题，

but I think it can do it the best

但是我认为如果我们每个人都参与进来

if it's all of us who are in control.

将能使它发挥最大的效用。

Thank you.

谢谢。