Visualizing the world's Twitter data - Jer Thorp

把推特上的數據影像化 - Jer Thorp

Transcriber: Andrea McDonough Reviewer: Bedirhan Cinar

譯者: Jephian Lin 審譯者: Coco Shen

A couple of years ago I started using Twitter,

幾年前，我開始使用推特（Twitter），

and one of the things that really charmed me about Twitter

而推特它其中一樣 真的很吸引我的一點

is that people would wake up in the morning

就是人們會一早起來

and they would say, "Good morning!"

然後他們會說：「早安！」

which I thought,

這讓我覺得，

I'm a Canadian,

因為我來自加拿大，

so I was a little bit,

所以我有一點點

I liked that politeness.

喜歡這種禮貌。

And so, I'm also a giant nerd,

而，我同時也是一個電腦狂，

and so I wrote a computer program

所以我寫了一個電腦程式，

that would record 24 hours of everybody on Twitter

它會記錄二十四小時中 在推特上每一位

saying, "Good morning!"

說「早安」的人。

And then I asked myself my favorite question,

接著我自己 一個我很喜歡的問題：

"What would that look like?"

「這看起來會是什麼樣子？」

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

嗯，結果就是， 我想它看起來像這樣。

Right, so we'd see this wave of people

好，所以我們可以看到 這股世界各地

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

在起床時說「早安」的人 的波動。

Now the green people, these are people that wake up

這些綠色的人，他們是

at around 8 o'clock in the morning,

大約八點起床的。

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

有誰在八點起床或是 在八點的時候說「早安」？

And the orange people,

而橘色的人，

they say, "Good morning!" around 9.

他們大約在九點的時候說「早安」。

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

接著是紅色的人， 他們大約在十點的時候說「早安」。

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

沒錯，十點的，十點的比八點的多。

And actually if you look at this map,

而事實上如果你看 這張地圖，

we can learn a little bit about how people wake up

你可以了解到 各地的人們

in different parts of the world.

起床時間有什麼不同。

People on the West Coast, for example,

比如說，西岸的人們

they wake up a little bit later

他們比東岸的人們

than those people on the East Coast.

晚起一些些。

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

但這不是推特上對話的全部，對吧？

We also get these really important tweets, like,

還有一些非常重要的推文：

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

「我剛抵達奧蘭多！！［飛機圖，飛機圖］」

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

或是「我剛抵達德州！［驚嘆號］」

Or "I just landed in Honduras!"

或「我剛抵達洪都拉斯！」

These lists, they go on and on and on,

這些清單，他們到這玩 到那玩，

all these people, right?

都這些人，對吧？

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

所以，表面上， 這些人只是告訴我們

something about how they're traveling.

一些他們旅行的事。

But we know the truth, don't we?

但我們知道事實，對吧？

These people are show-offs!

他們在炫耀！

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

他們在炫耀他們在開普敦 我卻不是。

So I thought, how can we take this vanity

所以我想，我們可以如何 利用這虛榮心

and turn it into utility?

而轉成有用的東西？

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

所以我用了 跟「早安」類似的方法，

I mapped all those people's trips

我把這些人的旅程 標在地圖上，

because I know where they're landing,

因為我知道他們目的地在哪。

they just told me,

是他們說的，

and I know where they live

而我知道他們住哪，

because they share that information on their Twitter profile.

因為他們在推特上 分享他們的個人資訊。

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

所以我能夠在 三十六小時的推特上做的事

is create a model of how people are traveling

就是建立一個 全世界的人們

around the world during that 36 hours.

在三十六小時內 旅行的模型。

And this is kind of a prototype

這其實是一個原型，

because I think if we listen to everybody

因為我想如果我們聆聽

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

人們在推特、臉書、 各種社群網站的紀錄，

we'd actually get a pretty clear picture

我們就可以得到，

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

人們如何旅行的清楚畫面，

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

這對科學家來說 將會非常有用，

particularly those who are studying how disease is spread.

尤其是對那些 研究疾病如何傳播的人來說。

So, I work upstairs in the New York Times,

所以，我在紐約時報樓上工作，

and for the last two years,

在過去兩年裡，

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

我們一直在做一個叫做瀑布（Cascade）的專案，

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

在某些方面來說 跟這個很像。

But instead of modeling how people move,

但我們不是為人們如何移動 建構模型，

we're modeling how people talk.

而是為人們如何對話 建構模型。

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

我們在看 一個像這樣的對話。

Well, here's an example.

嗯，這是一個例子。

This is a discussion around an article called,

這是關於一篇文章的討論。 這篇文章叫作

"The Island Where People Forget to Die".

「人們忘記死亡的島嶼」

It's about an island in Greece where people live

是在講希臘的一座島嶼，

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

上面住的人都 非常、非常、非常長壽。

And what we're seeing here

而我們可以看到的

is we're seeing a conversation that's stemming

是一連串從底部、

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

左下角的第一篇推文 衍生出的討論串。

So we get to see the scope of this conversation

所以我們可以看到 這個討論串的廣度，

over about 9 hours right now,

現在是九小時左右的樣子，

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

它會在幾秒後 蔓延成十二小時的樣子。

But, we can also see what that conversation

而，我們也可以用

looks like in three dimensions.

三度空間的方式 看這討論串。

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

而這立體的影像 對我們來說會更有用。

As humans, we are really used to things

身為人類，我們已經習慣

that are structured as three dimensions.

事物處於立體的狀態。

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

所以，我們可以看到 一些討論串的分枝，

we can find out what exactly happened.

我們可以看到 到底發生哪些事情。

And this is an interactive, exploratory tool

這是一個互動的、 探索性的工具，

so we can go through every step in the conversation.

所以我們可以 點開每部份的討論。

We can look at who the people were,

我們可以看到 發文的人們是誰、

what they said,

說了什麼、

how old they are,

年紀多大、

where they live,

住在哪裡、

who follows them,

誰跟著推文了，

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

還有許多、許多、許多。

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

所以，紐約時報每個月發表了 大約 6,500 個文章，

and we can model every single one

而我們會為 每個文章的討論串

of the conversations that happen around them.

以及相關發生的事情 建構模型。

And they look somewhat different.

而它們看起來有點不一樣。

Depending on the story

跟文章內容有關，

and depending on how fast people are talking about it

也取決於人們 有多快會得到訊息、

and how far the conversation spreads,

或是討論串有傳得多遠，

these structures, which I call these conversational architectures,

這些結構， 我把它叫做討論結構，

end up looking different.

它們看起來都不一樣。

So, these projects that I've shown you,

所以， 這些我給你看的專案，

I think they all involve the same thing:

我想他們都 涉及同一件事：

we can take small pieces of data

我們把小量的數據

and by putting them together,

把它們放在一起，

we can generate more value,

就可以產生更多價值，

we can do more exciting things with them.

用它們做些更有趣的事。

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

到目前為止， 我們只談到推特而已，對吧？

And Twitter isn't all the data.

而可用的數據並不只有推特而已。

We learned a moment ago

一些日子前， 我們知道

that there is tons and tons,

還有很多、很多、

tons more data out there.

很多的數據可用。

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

特別的是，我希望你們 想看看其中一種數據，

because all of you guys,

因為你們所有人、

everybody in this audience, we,

在場的所有人，我們、

we, me as well,

我們，我也是，

are data-making machines.

都是數據製造機。

We are producing data all the time.

我們隨時都在製造資訊。

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

我們之中每一個人， 都在製造數據。

Somebody else, though, is storing that data.

雖然，其它有些人 是在儲存數據。

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

通常我們對一些公司付出信任 所以將數據儲存在那，

but what I want to suggest here

但我想要建議的是

is that rather than putting our trust

與其相信這些為我們

in companies to store that data,

保存數據的公司，

we should put the trust in ourselves

我們更應該相信自己

because we actually own that data.

因為我們是這些數據的擁有人。

Right, that is something we should remember.

沒錯，我們該記住這件事。

Everything that someone else measures about you,

每一樣別人拿來評量你的標準，

you actually own.

其實是你擁有的。

So, it's my hope,

所以，我的希望是，

maybe because I'm a Canadian,

也許是因為我是個加拿大人，

that all of us can come together

我希望我們所有人 帶著這些

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

我們所儲存的珍貴的數據 聚在一起，

and we can collectively launch that data

然後我們可以 一起分享這些數據

toward some of the world's most difficulty problems

來解決這世上一些 最困難的問題，

because big data can solve big problems,

因為大的數據 可以解決大的問題，

but I think it can do it the best

但我想最好的狀況應是

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

我們眾人自己主導這件事情。

Thank you.

謝謝。