ABOUT THE SPEAKER
Jennifer Golbeck - Computer scientist
As the director of the Human-Computer Interaction Lab at the University of Maryland, Jennifer Golbeck studies how people use social media -- and thinks about ways to improve their interactions.

Why you should listen

Jennifer Golbeck is an associate professor in the College of Information Studies at the University of Maryland, where she also moonlights in the department of computer science. Her work invariably focuses on how to enhance and improve the way that people interact with their own information online. "I approach this from a computer science perspective and my general research hits social networks, trust, web science, artificial intelligence, and human-computer interaction," she writes.

Author of the 2013 book, Analyzing the Social Web, Golbeck likes nothing more than to immerse herself in the inner workings of the Internet tools so many millions of people use daily, to understand the implications of our choices and actions. Recently, she has also been working to bring human-computer interaction ideas to the world of security and privacy systems.

More profile about the speaker
Jennifer Golbeck | Speaker | TED.com
TEDxMidAtlantic 2013

Jennifer Golbeck: Your social media "likes" expose more than you think

珍妮佛.戈爾貝克: 炸馬鈴薯圈:社群點「讚」透露比你想像中更多的訊息

Filmed:
2,366,837 views

你喜歡炸馬鈴薯圈嗎?曾經到粉絲頁上按讚嗎?這篇演講揭露了關於臉書(以及其他網站)可以從你隨機的讚以及分享中獲得什麼資訊。電腦科學家珍妮佛.戈爾貝克解釋了其中的原因,這些科技應用並不那麼好,還有為什麼她認為我們應該把對訊息的控制權交回給正當的主人。
- Computer scientist
As the director of the Human-Computer Interaction Lab at the University of Maryland, Jennifer Golbeck studies how people use social media -- and thinks about ways to improve their interactions. Full bio

Double-click the English transcript below to play the video.

00:12
If you remember記得 that first decade of the web捲筒紙,
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如果你還記得網路出現的頭十年,
00:14
it was really a static靜態的 place地點.
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當時是一個很靜態的環境。
00:16
You could go online線上, you could look at pages網頁,
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你可以上網、瀏覽網頁,
00:19
and they were put up either by organizations組織
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這些網站或許是由一些機構製作,
00:21
who had teams球隊 to do it
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這些機構有自己的團隊,
00:23
or by individuals個人 who were really tech-savvy技術嫻熟
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或是當時很懂科技的人製作的。
00:25
for the time.
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00:27
And with the rise上升 of social社會 media媒體
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隨著社交媒體、
00:28
and social社會 networks網絡 in the early 2000s,
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社交網路在 21 世紀初期的興起,
00:31
the web捲筒紙 was completely全然 changed
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網路世界完全改變了。
00:33
to a place地點 where now the vast廣大 majority多數 of content內容
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現在的網路有很多內容
我們互動的內容是由網路用戶放上網的,
00:36
we interact相互作用 with is put up by average平均 users用戶,
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00:40
either in YouTubeYouTube的 videos視頻 or blog博客 posts帖子
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不管是 YouTube 上的影片或者部落格,
00:42
or product產品 reviews評論 or social社會 media媒體 postings帖子.
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抑或是商品評價或者社交媒體的文章。
00:46
And it's also become成為 a much more interactive互動 place地點,
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除此之外,網路也多了很多互動。
00:48
where people are interacting互動 with others其他,
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人們在網絡上互動,
00:51
they're commenting評論, they're sharing分享,
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他們評論、分享,
00:52
they're not just reading.
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而不僅是看看而已。
00:54
So FacebookFacebook的 is not the only place地點 you can do this,
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臉書不是唯一一個
能做這些事的網站,
00:56
but it's the biggest最大,
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但它是最大的。
00:57
and it serves供應 to illustrate說明 the numbers數字.
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我們可以通過臉書
來判斷使用人數。
00:59
FacebookFacebook的 has 1.2 billion十億 users用戶 per month.
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臉書每個月的用戶高達 12 億。
01:02
So half the Earth's地球 Internet互聯網 population人口
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也就是說全球一半的網民
01:04
is using運用 FacebookFacebook的.
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都在使用臉書。
01:06
They are a site現場, along沿 with others其他,
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這個網站,還有其他的網站,
01:08
that has allowed允許 people to create創建 an online線上 persona人物
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讓網民能創建網路上的個人形象
01:11
with very little technical技術 skill技能,
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而且無需太多的技術即可操作。
01:13
and people responded回應 by putting huge巨大 amounts
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用戶反應熱烈,上傳大量的
01:15
of personal個人 data數據 online線上.
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個人訊息到網路上。
01:17
So the result結果 is that we have behavioral行為的,
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這樣一來我們就有了有關行為、
01:20
preference偏愛, demographic人口 data數據
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偏好、地理數據,
01:22
for hundreds數以百計 of millions百萬 of people,
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提供給成千上萬的人,
01:24
which哪一個 is unprecedented史無前例 in history歷史.
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這是史無前例的。
01:26
And as a computer電腦 scientist科學家,
what this means手段 is that
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作為電腦科學家,這就意味著
01:29
I've been able能夠 to build建立 models楷模
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我可以建立很多模型
01:30
that can predict預測 all sorts排序 of hidden attributes屬性
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用來推測各種隱藏特性,
01:32
for all of you that you don't even know
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而你們自己可能都不知道
你們分享的訊息透露了這些特性。
01:35
you're sharing分享 information信息 about.
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01:37
As scientists科學家們, we use that to help
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科學家利用這些數據來改善
01:39
the way people interact相互作用 online線上,
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網民在網路上的互動,
01:41
but there's less altruistic利他 applications應用,
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但網路也有一些
沒那麼利他主義的應用,
01:44
and there's a problem問題 in that users用戶 don't really
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我們面臨一個問題,
那就是網路用戶並不真正
01:46
understand理解 these techniques技術 and how they work,
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了解這些網路技術、它們的運作原理,
01:49
and even if they did, they don't
have a lot of control控制 over it.
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而且即使他們懂,
也沒什麼辦法控制其影響。
所以我今天想和你們分享的,
01:52
So what I want to talk to you about today今天
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01:53
is some of these things that we're able能夠 to do,
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是我們力所能及、可控制的一些事情,
01:56
and then give us some ideas思路
of how we might威力 go forward前鋒
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給大家一些想法,看看我們如何發展才能
01:59
to move移動 some control控制 back into the hands of users用戶.
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把部分控制權交回到網路用戶的手上。
02:02
So this is Target目標, the company公司.
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這個是 Target 公司。
02:03
I didn't just put that logo商標
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我不是沒事把 Target 的標誌放在
02:05
on this poor較差的, pregnant woman's女人的 belly肚皮.
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這個可憐孕婦的肚子上。
02:07
You may可能 have seen看到 this anecdote軼事 that was printed印刷的
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你可能讀過一個小故事,刊登在
02:09
in Forbes福布斯 magazine雜誌 where Target目標
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富比士雜誌。故事提到 Target
02:11
sent發送 a flyer傳單 to this 15-year-old-歲 girl女孩
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發了張傳單給一位 15 歲的女孩。
02:13
with advertisements廣告 and coupons優惠券
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上面的廣告和折價卷
02:15
for baby寶寶 bottles瓶子 and diapers尿布 and cribs嬰兒床
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都是嬰兒奶瓶、尿布、嬰兒床的。
02:17
two weeks before she told her parents父母
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這還是在她告訴她父親
02:19
that she was pregnant.
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自己懷孕了之前兩週的事。
02:21
Yeah, the dad was really upset煩亂.
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是的,她的父親很難過。
02:24
He said, "How did Target目標 figure數字 out
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那為什麼 Target 知道
02:25
that this high school學校 girl女孩 was pregnant
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在這高中女生告訴父母她懷孕以前,
就已經先知道了呢?
02:27
before she told her parents父母?"
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02:29
It turns out that they have the purchase採購 history歷史
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原來,Target 有購物記錄,
02:32
for hundreds數以百計 of thousands數千 of customers顧客
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記錄成千上萬網路顧客的購物歷史,
02:34
and they compute計算 what they
call a pregnancy懷孕 score得分了,
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而且他們還有一個叫做
“懷孕分數”的計算系統,
02:37
which哪一個 is not just whether是否 or
not a woman's女人的 pregnant,
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這個系統不只計算一位女性是否懷孕,
02:39
but what her due應有 date日期 is.
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還有她們的預產期。
02:41
And they compute計算 that
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另外,他們不僅探討一些很明顯的資訊,
02:42
not by looking at the obvious明顯 things,
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02:44
like, she's buying購買 a crib嬰兒床 or baby寶寶 clothes衣服,
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比如說購買了一張嬰兒床、嬰兒服,
02:46
but things like, she bought more vitamins維生素
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還會計算她買了比平時多的維他命,
02:49
than she normally一般 had,
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02:51
or she bought a handbag手提包
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或者是她買了一個
大小足夠放下尿布的包包。
02:52
that's big enough足夠 to hold保持 diapers尿布.
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02:54
And by themselves他們自己, those purchases購買 don't seem似乎
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對購買者來說,
他們並不覺得這些購物訊息
02:56
like they might威力 reveal揭示 a lot,
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透露很多隱私,
02:59
but it's a pattern模式 of behavior行為 that,
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但其實這是一種行為模式,
03:01
when you take it in the context上下文
of thousands數千 of other people,
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當你把和成千上萬
網友的資料放在一起看,
03:04
starts啟動 to actually其實 reveal揭示 some insights見解.
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其實就能推測出很多東西。
03:06
So that's the kind of thing that we do
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所以這些就是我們所做的事情,
03:08
when we're predicting預測 stuff東東
about you on social社會 media媒體.
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我們在社群網站上
推測與你們相關的東西。
03:11
We're looking for little
patterns模式 of behavior行為 that,
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我們要找的行為模式是,
03:14
when you detect檢測 them among其中 millions百萬 of people,
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當你們從上百萬人身上發現這種模式,
03:16
lets讓我們 us find out all kinds of things.
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我們就能找到所有相關的事情。
03:19
So in my lab實驗室 and with colleagues同事,
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所以我和實驗室的同事們,
03:21
we've我們已經 developed發達 mechanisms機制 where we can
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開發了多種機制,幫助我們
03:22
quite相當 accurately準確 predict預測 things
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較精確地推斷很多事情,
03:24
like your political政治 preference偏愛,
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像是你的政治傾向、
03:26
your personality個性 score得分了, gender性別, sexual有性 orientation方向,
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性格測試分數、性別、性取向、
03:29
religion宗教, age年齡, intelligence情報,
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宗教信仰、年齡、智力,
03:32
along沿 with things like
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同時還有像是
03:34
how much you trust相信 the people you know
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你對認識的人有多信任、
03:36
and how strong強大 those relationships關係 are.
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你們的關係有多緊密等。
03:38
We can do all of this really well.
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所有這些我們都可以做得很好。
03:39
And again, it doesn't come from what you might威力
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而且,這些都不是來自於
你會認為是明顯的訊息。
03:41
think of as obvious明顯 information信息.
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03:44
So my favorite喜愛 example is from this study研究
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我最喜歡舉的一個例子
03:46
that was published發表 this year
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是一個今年發表的研究
03:47
in the Proceedings論文集 of the National國民 Academies學院.
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刊在《美國國家科學院院刊》上。
03:49
If you Google谷歌 this, you'll你會 find it.
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Google 一下就能查到。
03:50
It's four pages網頁, easy簡單 to read.
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研究只有四頁紙,很容易讀。
03:52
And they looked看著 at just people's人們 FacebookFacebook的 likes喜歡,
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他們僅是研究了用戶在臉書的點讚,
03:55
so just the things you like on FacebookFacebook的,
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只是你在臉書上點讚的內容,
03:57
and used that to predict預測 all these attributes屬性,
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用這些點讚的內容
來推斷所有這些特性,
03:59
along沿 with some other ones那些.
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以及其他的資訊。
04:01
And in their paper they listed上市 the five likes喜歡
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在調查中,他們列出了五類的讚,
04:04
that were most indicative指示 of high intelligence情報.
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這些讚最能表明高智商的用戶。
04:07
And among其中 those was liking喜歡 a page
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這其中還包括
到炸馬鈴薯圈頁面點讚。(笑聲)
04:09
for curly捲曲 fries薯條. (Laughter笑聲)
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04:11
Curly捲曲 fries薯條 are delicious美味的,
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炸馬鈴薯圈是好吃,
04:13
but liking喜歡 them does not necessarily一定 mean
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但是到這頁面按讚不表示
04:15
that you're smarter聰明 than the average平均 person.
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你就比一般人聰明。
04:17
So how is it that one of the strongest最強 indicators指標
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到底為什麼,
最能體現你智商指數的指標之一
04:21
of your intelligence情報
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04:22
is liking喜歡 this page
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是到一個頁面按讚,
04:24
when the content內容 is totally完全 irrelevant不相干
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即使頁面的內容完全無關於
04:26
to the attribute屬性 that's being存在 predicted預料到的?
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要推斷的特性?
04:28
And it turns out that we have to look at
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結論是,我們需要參考
04:30
a whole整個 bunch of underlying底層 theories理論
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很多背後的理論
04:32
to see why we're able能夠 to do this.
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來了解為什麼我們能夠做到這點。
04:34
One of them is a sociological社會學的
theory理論 called homophily趨同性,
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其中一個就是社會學理論,叫同質相吸,
04:37
which哪一個 basically基本上 says people are
friends朋友 with people like them.
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指的是人們通常
和與自己相像的人交朋友。
04:40
So if you're smart聰明, you tend趨向 to
be friends朋友 with smart聰明 people,
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所以如果你聰明,
你會和聰明的人交朋友,
04:42
and if you're young年輕, you tend趨向
to be friends朋友 with young年輕 people,
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如果你年輕,
你會和年輕人交朋友,
04:45
and this is well established既定
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這個理論是經過驗證的,
04:46
for hundreds數以百計 of years年份.
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多年來大家都肯定。
04:48
We also know a lot
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我們還知道很多
04:49
about how information信息 spreads利差 through通過 networks網絡.
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關於訊息在網路上如何傳播。
04:52
It turns out things like viral病毒 videos視頻
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我們發現病毒影片、
04:54
or FacebookFacebook的 likes喜歡 or other information信息
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臉書按讚或是其他訊息
04:56
spreads利差 in exactly究竟 the same相同 way
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傳播的方式完全和
04:58
that diseases疾病 spread傳播 through通過 social社會 networks網絡.
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病毒透過社群網站傳播的方式一樣。
05:01
So this is something we've我們已經 studied研究 for a long time.
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這是我們研究了很長時間的東西,
05:02
We have good models楷模 of it.
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我們有很好的模型。
05:04
And so you can put those things together一起
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所以如果你們把這些模型都放在一起,
05:06
and start開始 seeing眼看 why things like this happen發生.
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就能了解為何這樣的事情會發生了。
05:09
So if I were to give you a hypothesis假設,
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如果要給各位一個假設,
05:11
it would be that a smart聰明 guy started開始 this page,
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那就是一個聰明的人
建立了一個粉絲頁,
05:14
or maybe one of the first people who liked喜歡 it
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或者剛開始幾個去按讚的人
05:16
would have scored進球 high on that test測試.
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在智力測試上得了高分,
05:18
And they liked喜歡 it, and their friends朋友 saw it,
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他們給這個頁面點了讚,
當他們的朋友看見了,
05:20
and by homophily趨同性, we know that
he probably大概 had smart聰明 friends朋友,
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根據同質相吸的原理,我們知道
這些人的朋友可能也很聰明,
05:23
and so it spread傳播 to them,
and some of them liked喜歡 it,
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當訊息傳給他們,
有些人也會給這個頁面點讚,
05:26
and they had smart聰明 friends朋友,
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而他們又有聰明的朋友,
05:28
and so it spread傳播 to them,
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訊息接著傳出去,
05:28
and so it propagated傳播 through通過 the network網絡
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這樣一來,就在網路上傳開了,
05:30
to a host主辦 of smart聰明 people,
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傳給一群聰明的人,
05:33
so that by the end結束, the action行動
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如此,到最後
05:35
of liking喜歡 the curly捲曲 fries薯條 page
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給炸馬鈴薯圈頁面點讚的行為
05:37
is indicative指示 of high intelligence情報,
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就成了高智商的指標,
05:39
not because of the content內容,
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並不是因為頁面的內容,
05:41
but because the actual實際 action行動 of liking喜歡
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而是因為點讚的這一行為
05:43
reflects反映 back the common共同 attributes屬性
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反映了做這件事情的人的
05:45
of other people who have doneDONE it.
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共同特性。
05:48
So this is pretty漂亮 complicated複雜 stuff東東, right?
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所以這還是挺複雜的,是吧?
05:51
It's a hard thing to sit down and explain說明
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要坐下來跟普通用戶解釋是困難的,
05:53
to an average平均 user用戶, and even if you do,
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而且即使我們分析了,
05:56
what can the average平均 user用戶 do about it?
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對普通用戶們又有什麼用呢?
05:58
How do you know that
you've liked喜歡 something
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你們怎麼知道到某個粉絲頁按讚
06:00
that indicates指示 a trait特徵 for you
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能夠反映出你的特性,
06:01
that's totally完全 irrelevant不相干 to the
content內容 of what you've liked喜歡?
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而這特性又和你按讚的內容
完全無關呢?
06:05
There's a lot of power功率 that users用戶 don't have
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很多的權力用戶都沒有,
06:08
to control控制 how this data數據 is used.
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他們沒法控制這些數據的使用。
06:10
And I see that as a real真實
problem問題 going forward前鋒.
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我認為這是我們繼續發展
所面臨的真正困難。
06:13
So I think there's a couple一對 paths路徑
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所以我想到了幾條途徑
06:15
that we want to look at
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我們可以參考,
06:16
if we want to give users用戶 some control控制
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看能不能給用戶一些
06:18
over how this data數據 is used,
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控制這些數據的方法。
06:20
because it's not always going to be used
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因為這些數據並不總是
06:21
for their benefit效益.
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能替用戶帶來益處。
06:23
An example I often經常 give is that,
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我常舉例說,
06:24
if I ever get bored無聊 being存在 a professor教授,
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如果我厭倦當教授,
06:26
I'm going to go start開始 a company公司
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我要開個公司
06:28
that predicts預測 all of these attributes屬性
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去推斷所有這些用戶特性,
06:29
and things like how well you work in teams球隊
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像是你的團隊合作、
06:31
and if you're a drug藥物 user用戶, if you're an alcoholic酒精.
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嗑不嗑藥、是不是酒鬼。
06:33
We know how to predict預測 all that.
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我們知道如何去推斷這些訊息。
06:35
And I'm going to sell reports報告
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接著我就要把這些報告
06:36
to H.R. companies公司 and big businesses企業
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賣給人力資源公司或者大企業
06:39
that want to hire聘請 you.
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就是那些將要雇你的人。
06:41
We totally完全 can do that now.
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我們現在完全可以做到這些。
06:42
I could start開始 that business商業 tomorrow明天,
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我明天就可以開始做,
06:44
and you would have
absolutely絕對 no control控制
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而且你完全沒法控制
06:46
over me using運用 your data數據 like that.
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我這樣使用數據的行為。
06:48
That seems似乎 to me to be a problem問題.
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這在我看來是一個問題。
06:50
So one of the paths路徑 we can go down
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所以我們能選擇的
其中一條途徑就是
06:52
is the policy政策 and law path路徑.
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政策和法律的制定。
06:54
And in some respects尊重, I think
that that would be most effective有效,
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在某種程度上,
我認為這將是最有效的方法,
06:57
but the problem問題 is we'd星期三
actually其實 have to do it.
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但問題是我們必須得實際執行。
07:00
Observing觀察 our political政治 process處理 in action行動
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透過觀察我們的政治進程,
07:03
makes品牌 me think it's highly高度 unlikely不會
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讓我意識到我們很難
07:05
that we're going to get a bunch of representatives代表
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集合一群代表,
07:07
to sit down, learn學習 about this,
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讓他們坐下來了解這件事,
07:09
and then enact制定 sweeping籠統的 changes變化
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然後開始進行大規模改變,
07:11
to intellectual知識分子 property屬性 law in the U.S.
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修改美國的知識產權法律
07:13
so users用戶 control控制 their data數據.
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以讓用戶有權控制他們的數據。
07:16
We could go the policy政策 route路線,
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我們可以走政策道路,
07:17
where social社會 media媒體 companies公司 say,
197
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讓社群公司表態,
07:18
you know what? You own擁有 your data數據.
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1402
「好,你們擁有自己的數據。
07:20
You have total control控制 over how it's used.
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你們能完全地控制對它們的使用。」
07:22
The problem問題 is that the revenue收入 models楷模
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問題在於
多數社交媒體的收益模式
07:24
for most social社會 media媒體 companies公司
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07:26
rely依靠 on sharing分享 or exploiting利用
users'用戶' data數據 in some way.
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某種程度上仰賴
分享或利用用戶的數據。
07:30
It's sometimes有時 said of FacebookFacebook的 that the users用戶
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有人說臉書的用戶
07:32
aren't the customer顧客, they're the product產品.
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不是顧客,而是產品。
07:34
And so how do you get a company公司
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2714
所以你怎麼可能讓一間公司
07:37
to cede放棄 control控制 of their main主要 asset財富
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放棄對他們主要收入的控制
07:39
back to the users用戶?
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把控制權還給用戶呢?
07:41
It's possible可能, but I don't think it's something
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這是有可能的,但我不認為
07:42
that we're going to see change更改 quickly很快.
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我們能很快看到這一改變。
07:45
So I think the other path路徑
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所以我認為另外一條途徑
07:46
that we can go down that's
going to be more effective有效
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一條更有效的途徑,
07:48
is one of more science科學.
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是更科學的途徑。
07:50
It's doing science科學 that allowed允許 us to develop發展
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正是透過科學,我們才能開發
07:52
all these mechanisms機制 for computing計算
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所有的這些機制首先用於計算個人數據
07:54
this personal個人 data數據 in the first place地點.
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07:56
And it's actually其實 very similar類似 research研究
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事實上,有個很類似的研究,
07:58
that we'd星期三 have to do
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08:00
if we want to develop發展 mechanisms機制
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如果我們要發明一些機制
08:02
that can say to a user用戶,
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是可以對用戶說
08:04
"Here's這裡的 the risk風險 of that action行動 you just took."
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「這是你剛才所做的行為
要面臨的風險。」
08:06
By liking喜歡 that FacebookFacebook的 page,
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藉由臉書按讚,
08:08
or by sharing分享 this piece of personal個人 information信息,
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或者是分享私人資訊,
08:10
you've now improved改善 my ability能力
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你現在給了我更多能力
08:12
to predict預測 whether是否 or not you're using運用 drugs毒品
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去推斷你是否嗑藥
08:14
or whether是否 or not you get
along沿 well in the workplace職場.
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或者你是否和同事相處融洽。
08:17
And that, I think, can affect影響 whether是否 or not
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我認為這些會影響
08:19
people want to share分享 something,
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人們是否願意分享事情、
08:20
keep it private私人的, or just keep it offline離線 altogether.
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還是設為隱私,或者是完全不放上網絡。
08:24
We can also look at things like
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我們還可以研究一些像是
08:25
allowing允許 people to encrypt加密 data數據 that they upload上載,
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讓用戶可以加密他們上傳的數據,
08:28
so it's kind of invisible無形 and worthless無用
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所以對像是臉書的網站,
這是隱形而且無用的,
08:30
to sites網站 like FacebookFacebook的
232
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08:31
or third第三 party派對 services服務 that access訪問 it,
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或者是第三方服務網站也是如此。
08:34
but that select選擇 users用戶 who the person who posted發布 it
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但是用戶可選擇上傳的東西
08:37
want to see it have access訪問 to see it.
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要讓誰有權可以看到。
08:40
This is all super exciting扣人心弦 research研究
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如果我們從知識的角度去看,
08:42
from an intellectual知識分子 perspective透視,
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這些都是非常令人興奮的研究,
08:43
and so scientists科學家們 are going to be willing願意 to do it.
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所以說科學家會願意做相關的研究。
08:45
So that gives us an advantage優點 over the law side.
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這比起法律的途徑,
給了我們更多的好處。
08:49
One of the problems問題 that people bring帶來 up
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當我談到這個的時候,
08:51
when I talk about this is, they say,
241
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人們常會提出一個疑問,
08:52
you know, if people start開始
keeping保持 all this data數據 private私人的,
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2646
你知道,如果人們開始把這些數據都保密了,
08:55
all those methods方法 that you've been developing發展
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你們一直在開發的這些
08:57
to predict預測 their traits性狀 are going to fail失敗.
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用來推斷他們特性的方法都將失效,
09:00
And I say, absolutely絕對, and for me, that's success成功,
245
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我回答說,完全正確,
但對我來說,那就是成功。
09:03
because as a scientist科學家,
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因為身為一名科學家,
09:05
my goal目標 is not to infer推斷 information信息 about users用戶,
247
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我的目標不是要推斷用戶的資訊,
09:09
it's to improve提高 the way people interact相互作用 online線上.
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而是要改進人們在網路互動的方式。
09:11
And sometimes有時 that involves涉及
inferring推斷 things about them,
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有時候這包括推斷關於他們的事情,
09:15
but if users用戶 don't want me to use that data數據,
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但如果用戶不想要我使用這些數據,
09:18
I think they should have the right to do that.
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我認為他們有權利這麼做。
09:20
I want users用戶 to be informed通知 and consenting同意
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我希望用戶們可以知道且同意
09:22
users用戶 of the tools工具 that we develop發展.
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我們一直開發這些工具。
09:24
And so I think encouraging鼓舞人心的 this kind of science科學
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所以,我認為推廣這類科學、
09:27
and supporting支持 researchers研究人員
255
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支持研究者,
09:29
who want to cede放棄 some of that control控制 back to users用戶
256
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支持那些希望把控制權
交回到用戶手中,
09:32
and away from the social社會 media媒體 companies公司
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從社群媒體公司
拿回這些權利的研究者,
09:34
means手段 that going forward前鋒, as these tools工具 evolve發展
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意味著隨著這些工具進化和發展,
我們是向前發展的。
09:37
and advance提前,
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1476
09:38
means手段 that we're going to have an educated博學
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我們將有一組教育程度更高、
更有力的用戶數據,
09:40
and empowered授權 user用戶 base基礎,
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09:41
and I think all of us can agree同意
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我相信大家都會認同
09:42
that that's a pretty漂亮 ideal理想 way to go forward前鋒.
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朝此理想的發展方式前進。
09:45
Thank you.
264
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謝謝。
09:47
(Applause掌聲)
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(掌聲)
Translated by Adrienne Lin
Reviewed by Ying Ru Wu

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ABOUT THE SPEAKER
Jennifer Golbeck - Computer scientist
As the director of the Human-Computer Interaction Lab at the University of Maryland, Jennifer Golbeck studies how people use social media -- and thinks about ways to improve their interactions.

Why you should listen

Jennifer Golbeck is an associate professor in the College of Information Studies at the University of Maryland, where she also moonlights in the department of computer science. Her work invariably focuses on how to enhance and improve the way that people interact with their own information online. "I approach this from a computer science perspective and my general research hits social networks, trust, web science, artificial intelligence, and human-computer interaction," she writes.

Author of the 2013 book, Analyzing the Social Web, Golbeck likes nothing more than to immerse herself in the inner workings of the Internet tools so many millions of people use daily, to understand the implications of our choices and actions. Recently, she has also been working to bring human-computer interaction ideas to the world of security and privacy systems.

More profile about the speaker
Jennifer Golbeck | Speaker | TED.com