Name: TED-Ed】將世界的Twitter數據可視化--Jer Thorp。 (【TED-Ed】Visualizing the world's Twitter data - Jer Thorp)
Uploaded: 2021-01-14T04:50:19.000Z
Duration: 5 min 42 s

動名詞

A couple of years ago I started using Twitter,

幾年前，我開始使用推特（Twitter），

過去簡單式

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

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

And then I asked myself my favorite question,

接著我自己 一個我很喜歡的問題：

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。

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

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」？

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。

we can learn a little bit about how people wake up

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,

"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］」

這些清單，他們到這玩 到那玩，

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

所以，表面上， 這些人只是告訴我們

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

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

所以我用了 跟「早安」類似的方法，

我把這些人的旅程 標在地圖上，

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

because I think if we listen to everybody

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，

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

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，

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.

但我們不是為人們如何移動 建構模型，

而是為人們如何對話 建構模型。

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

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

It's about an island in Greece where people live

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

上面住的人都 非常、非常、非常長壽。

is we're seeing a conversation that's stemming

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，

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

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。

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，

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。

我們可以看到 發文的人們是誰、

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，

of the conversations that happen around them.

以及相關發生的事情 建構模型。

and depending on how fast people are talking about it

也取決於人們 有多快會得到訊息、

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TED-Ed】將世界的Twitter數據可視化--Jer Thorp。 (【TED-Ed】Visualizing the world's Twitter data - Jer Thorp)

approach

content

structure

audience