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
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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。
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. Full bio
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 that first decade of the web,
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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 place.
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當時是一個很靜態的環境。
00:16
You could go online, you could look at pages,
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你可以上網、瀏覽網頁，
00:19
and they were put up either by organizations
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這些網站或許是由一些機構製作，
00:21
who had teams to do it
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這些機構有自己的團隊，
00:23
or by individuals who were really tech-savvy
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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 of social media
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隨著社交媒體、
00:28
and social networks 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 was completely changed
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網路世界完全改變了。
00:33
to a place where now the vast majority of content
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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 with is put up by average users,
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00:40
either in YouTube videos or blog posts
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不管是 YouTube 上的影片或者部落格，
00:42
or product reviews or social media postings.
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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 a much more interactive place,
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除此之外，網路也多了很多互動。
00:48
where people are interacting with others,
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人們在網絡上互動，
00:51
they're commenting, they're sharing,
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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 Facebook is not the only place you can do this,
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臉書不是唯一一個
能做這些事的網站，
能做這些事的網站，
00:56
but it's the biggest,
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但它是最大的。
00:57
and it serves to illustrate the numbers.
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我們可以通過臉書
來判斷使用人數。
來判斷使用人數。
00:59
Facebook has 1.2 billion users 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 Internet population
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也就是說全球一半的網民
01:04
is using Facebook.
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都在使用臉書。
01:06
They are a site, along with others,
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這個網站，還有其他的網站，
01:08
that has allowed people to create an online persona
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讓網民能創建網路上的個人形象
01:11
with very little technical skill,
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而且無需太多的技術即可操作。
01:13
and people responded by putting huge amounts
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用戶反應熱烈，上傳大量的
01:15
of personal data online.
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個人訊息到網路上。
01:17
So the result is that we have behavioral,
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這樣一來我們就有了有關行為、
01:20
preference, demographic data
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偏好、地理數據，
01:22
for hundreds of millions of people,
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提供給成千上萬的人，
01:24
which is unprecedented in history.
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這是史無前例的。
01:26
And as a computer scientist,
what this means is that
what this means is that
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作為電腦科學家，這就意味著
01:29
I've been able to build models
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我可以建立很多模型
01:30
that can predict all sorts of hidden attributes
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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 information about.
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01:37
As scientists, 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 online,
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網民在網路上的互動，
01:41
but there's less altruistic applications,
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但網路也有一些
沒那麼利他主義的應用，
沒那麼利他主義的應用，
01:44
and there's a problem in that users 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
那就是網路用戶並不真正
01:46
understand these techniques 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 over it.
have a lot of control over it.
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而且即使他們懂，
也沒什麼辦法控制其影響。
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
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01:53
is some of these things that we're able 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
of how we might go forward
of how we might go forward
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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 some control back into the hands of users.
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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, the company.
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這個是 Target 公司。
02:03
I didn't just put that logo
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我不是沒事把 Target 的標誌放在
02:05
on this poor, pregnant woman's belly.
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這個可憐孕婦的肚子上。
02:07
You may have seen this anecdote that was printed
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你可能讀過一個小故事，刊登在
02:09
in Forbes magazine where Target
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富比士雜誌。故事提到 Target
02:11
sent a flyer to this 15-year-old girl
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發了張傳單給一位 15 歲的女孩。
02:13
with advertisements and coupons
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上面的廣告和折價卷
02:15
for baby bottles and diapers and cribs
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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
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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.
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是的，她的父親很難過。
02:24
He said, "How did Target figure out
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那為什麼 Target 知道
02:25
that this high school girl 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？
就已經先知道了呢？
02:27
before she told her parents?"
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02:29
It turns out that they have the purchase history
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原來，Target 有購物記錄，
02:32
for hundreds of thousands of customers
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記錄成千上萬網路顧客的購物歷史，
02:34
and they compute what they
call a pregnancy score,
call a pregnancy score,
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而且他們還有一個叫做
“懷孕分數”的計算系統，
“懷孕分數”的計算系統，
02:37
which is not just whether or
not a woman's pregnant,
not a woman's 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 date is.
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還有她們的預產期。
02:41
And they compute 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 things,
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02:44
like, she's buying a crib or baby clothes,
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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
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還會計算她買了比平時多的維他命，
02:49
than she normally had,
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02:51
or she bought a handbag
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或者是她買了一個
大小足夠放下尿布的包包。
大小足夠放下尿布的包包。
02:52
that's big enough to hold diapers.
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02:54
And by themselves, those purchases don't seem
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對購買者來說，
他們並不覺得這些購物訊息
他們並不覺得這些購物訊息
02:56
like they might reveal a lot,
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透露很多隱私，
02:59
but it's a pattern of behavior 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
of thousands of other people,
of thousands 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，
網友的資料放在一起看，
03:04
starts to actually reveal some insights.
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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 stuff
about you on social media.
about you on social media.
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我們在社群網站上
推測與你們相關的東西。
推測與你們相關的東西。
03:11
We're looking for little
patterns of behavior that,
patterns of behavior that,
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我們要找的行為模式是，
03:14
when you detect them among millions 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 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 and with colleagues,
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所以我和實驗室的同事們，
03:21
we've developed mechanisms where we can
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開發了多種機制，幫助我們
03:22
quite accurately predict things
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較精確地推斷很多事情，
03:24
like your political preference,
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像是你的政治傾向、
03:26
your personality score, gender, sexual orientation,
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性格測試分數、性別、性取向、
03:29
religion, age, intelligence,
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宗教信仰、年齡、智力，
03:32
along with things like
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同時還有像是
03:34
how much you trust the people you know
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你對認識的人有多信任、
03:36
and how strong those relationships 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
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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 information.
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03:44
So my favorite example is from this study
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我最喜歡舉的一個例子
03:46
that was published this year
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是一個今年發表的研究
03:47
in the Proceedings of the National Academies.
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刊在《美國國家科學院院刊》上。
03:49
If you Google this, you'll find it.
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Google 一下就能查到。
03:50
It's four pages, easy to read.
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研究只有四頁紙，很容易讀。
03:52
And they looked at just people's Facebook likes,
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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 Facebook,
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只是你在臉書上點讚的內容，
03:57
and used that to predict all these attributes,
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用這些點讚的內容
來推斷所有這些特性，
來推斷所有這些特性，
03:59
along with some other ones.
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以及其他的資訊。
04:01
And in their paper they listed the five likes
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在調查中，他們列出了五類的讚，
04:04
that were most indicative of high intelligence.
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這些讚最能表明高智商的用戶。
04:07
And among those was liking 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 fries. (Laughter)
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04:11
Curly fries are delicious,
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炸馬鈴薯圈是好吃，
04:13
but liking them does not necessarily mean
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但是到這頁面按讚不表示
04:15
that you're smarter than the average person.
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你就比一般人聰明。
04:17
So how is it that one of the strongest indicators
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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
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04:22
is liking this page
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是到一個頁面按讚，
04:24
when the content is totally irrelevant
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即使頁面的內容完全無關於
04:26
to the attribute that's being predicted?
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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 bunch of underlying theories
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很多背後的理論
04:32
to see why we're able 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
theory called homophily,
theory called homophily,
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其中一個就是社會學理論，叫同質相吸，
04:37
which basically says people are
friends with people like them.
friends 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。
和與自己相像的人交朋友。
04:40
So if you're smart, you tend to
be friends with smart people,
be friends with smart people,
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所以如果你聰明，
你會和聰明的人交朋友，
你會和聰明的人交朋友，
04:42
and if you're young, you tend
to be friends with young people,
to be friends with young people,
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如果你年輕，
你會和年輕人交朋友，
你會和年輕人交朋友，
04:45
and this is well established
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這個理論是經過驗證的，
04:46
for hundreds of years.
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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 spreads through networks.
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關於訊息在網路上如何傳播。
04:52
It turns out things like viral videos
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我們發現病毒影片、
04:54
or Facebook likes or other information
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臉書按讚或是其他訊息
04:56
spreads in exactly the same way
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傳播的方式完全和
04:58
that diseases spread through social networks.
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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 studied 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 of it.
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我們有很好的模型。
05:04
And so you can put those things together
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所以如果你們把這些模型都放在一起，
05:06
and start seeing why things like this happen.
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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,
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如果要給各位一個假設，
05:11
it would be that a smart guy started this page,
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那就是一個聰明的人
建立了一個粉絲頁，
建立了一個粉絲頁，
05:14
or maybe one of the first people who liked it
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或者剛開始幾個去按讚的人
05:16
would have scored high on that test.
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在智力測試上得了高分，
05:18
And they liked it, and their friends 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，
當他們的朋友看見了，
05:20
and by homophily, we know that
he probably had smart friends,
he probably had smart friends,
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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，
這些人的朋友可能也很聰明，
05:23
and so it spread to them,
and some of them liked it,
and some of them liked it,
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當訊息傳給他們，
有些人也會給這個頁面點讚，
有些人也會給這個頁面點讚，
05:26
and they had smart friends,
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而他們又有聰明的朋友，
05:28
and so it spread to them,
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訊息接著傳出去，
05:28
and so it propagated through the network
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這樣一來，就在網路上傳開了，
05:30
to a host of smart people,
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傳給一群聰明的人，
05:33
so that by the end, the action
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如此，到最後
05:35
of liking the curly fries page
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給炸馬鈴薯圈頁面點讚的行為
05:37
is indicative of high intelligence,
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就成了高智商的指標，
05:39
not because of the content,
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並不是因為頁面的內容，
05:41
but because the actual action of liking
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而是因為點讚的這一行為
05:43
reflects back the common attributes
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反映了做這件事情的人的
05:45
of other people who have done it.
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共同特性。
05:48
So this is pretty complicated stuff, 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
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要坐下來跟普通用戶解釋是困難的，
05:53
to an average user, and even if you do,
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而且即使我們分析了，
05:56
what can the average user 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 something
you've liked something
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你們怎麼知道到某個粉絲頁按讚
06:00
that indicates a trait for you
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能夠反映出你的特性，
06:01
that's totally irrelevant to the
content of what you've liked?
content of what you've liked?
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而這特性又和你按讚的內容
完全無關呢？
完全無關呢？
06:05
There's a lot of power that users don't have
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很多的權力用戶都沒有，
06:08
to control how this data 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
problem going forward.
problem going forward.
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我認為這是我們繼續發展
所面臨的真正困難。
所面臨的真正困難。
06:13
So I think there's a couple paths
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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 some control
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看能不能給用戶一些
06:18
over how this data 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.
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能替用戶帶來益處。
06:23
An example I often give is that,
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我常舉例說，
06:24
if I ever get bored being a professor,
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如果我厭倦當教授，
06:26
I'm going to go start a company
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我要開個公司
06:28
that predicts all of these attributes
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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
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像是你的團隊合作、
06:31
and if you're a drug user, if you're an alcoholic.
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嗑不嗑藥、是不是酒鬼。
06:33
We know how to predict 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
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接著我就要把這些報告
06:36
to H.R. companies and big businesses
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賣給人力資源公司或者大企業
06:39
that want to hire you.
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就是那些將要雇你的人。
06:41
We totally 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 that business tomorrow,
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我明天就可以開始做，
06:44
and you would have
absolutely no control
absolutely no control
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而且你完全沒法控制
06:46
over me using your data like that.
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我這樣使用數據的行為。
06:48
That seems to me to be a problem.
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這在我看來是一個問題。
06:50
So one of the paths we can go down
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所以我們能選擇的
其中一條途徑就是
其中一條途徑就是
06:52
is the policy and law path.
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政策和法律的制定。
06:54
And in some respects, I think
that that would be most effective,
that that would be most effective,
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在某種程度上，
我認為這將是最有效的方法，
我認為這將是最有效的方法，
06:57
but the problem is we'd
actually have to do it.
actually have to do it.
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但問題是我們必須得實際執行。
07:00
Observing our political process in action
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透過觀察我們的政治進程，
07:03
makes me think it's highly unlikely
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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
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集合一群代表，
07:07
to sit down, learn about this,
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讓他們坐下來了解這件事，
07:09
and then enact sweeping changes
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然後開始進行大規模改變，
07:11
to intellectual property law in the U.S.
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修改美國的知識產權法律
07:13
so users control their data.
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以讓用戶有權控制他們的數據。
07:16
We could go the policy route,
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我們可以走政策道路，
07:17
where social media companies say,
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讓社群公司表態，
07:18
you know what? You own your data.
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「好，你們擁有自己的數據。
07:20
You have total control 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 is that the revenue models
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問題在於
多數社交媒體的收益模式
07:24
for most social media companies
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07:26
rely on sharing or exploiting
users' data in some way.
users' data in some way.
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某種程度上仰賴
分享或利用用戶的數據。
分享或利用用戶的數據。
07:30
It's sometimes said of Facebook that the users
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有人說臉書的用戶
07:32
aren't the customer, they're the product.
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不是顧客，而是產品。
07:34
And so how do you get a company
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所以你怎麼可能讓一間公司
07:37
to cede control of their main asset
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放棄對他們主要收入的控制
07:39
back to the users?
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把控制權還給用戶呢？
07:41
It's possible, 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 quickly.
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我們能很快看到這一改變。
07:45
So I think the other path
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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
going to be more effective
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一條更有效的途徑，
07:48
is one of more science.
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是更科學的途徑。
07:50
It's doing science that allowed us to develop
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正是透過科學，我們才能開發
07:52
all these mechanisms for computing
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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 data in the first place.
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07:56
And it's actually very similar research
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事實上，有個很類似的研究，
07:58
that we'd have to do
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08:00
if we want to develop mechanisms
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如果我們要發明一些機制
08:02
that can say to a user,
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是可以對用戶說
08:04
"Here's the risk of that action you just took."
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「這是你剛才所做的行為
要面臨的風險。」
要面臨的風險。」
08:06
By liking that Facebook page,
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藉由臉書按讚，
08:08
or by sharing this piece of personal information,
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或者是分享私人資訊，
08:10
you've now improved my ability
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你現在給了我更多能力
08:12
to predict whether or not you're using drugs
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去推斷你是否嗑藥
08:14
or whether or not you get
along well in the workplace.
along well in the workplace.
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或者你是否和同事相處融洽。
08:17
And that, I think, can affect whether or not
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我認為這些會影響
08:19
people want to share something,
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人們是否願意分享事情、
08:20
keep it private, or just keep it offline 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 people to encrypt data that they upload,
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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 and worthless
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所以對像是臉書的網站，
這是隱形而且無用的，
這是隱形而且無用的，
08:30
to sites like Facebook
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08:31
or third party services that access it,
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或者是第三方服務網站也是如此。
08:34
but that select users who the person who posted 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 to see it.
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要讓誰有權可以看到。
08:40
This is all super exciting research
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如果我們從知識的角度去看，
08:42
from an intellectual perspective,
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這些都是非常令人興奮的研究，
08:43
and so scientists are going to be willing 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 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。
給了我們更多的好處。
08:49
One of the problems that people bring up
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當我談到這個的時候，
08:51
when I talk about this is, they say,
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人們常會提出一個疑問，
08:52
you know, if people start
keeping all this data private,
keeping all this data private,
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你知道，如果人們開始把這些數據都保密了，
08:55
all those methods that you've been developing
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你們一直在開發的這些
08:57
to predict their traits are going to fail.
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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, and for me, that's success,
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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,
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因為身為一名科學家，
09:05
my goal is not to infer information about users,
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我的目標不是要推斷用戶的資訊，
09:09
it's to improve the way people interact online.
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而是要改進人們在網路互動的方式。
09:11
And sometimes that involves
inferring things about them,
inferring 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 don't want me to use that data,
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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 to be informed and consenting
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我希望用戶們可以知道且同意
09:22
users of the tools that we develop.
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我們一直開發這些工具。
09:24
And so I think encouraging this kind of science
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所以，我認為推廣這類科學、
09:27
and supporting researchers
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支持研究者，
09:29
who want to cede some of that control back to users
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支持那些希望把控制權
交回到用戶手中，
交回到用戶手中，
09:32
and away from the social media companies
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從社群媒體公司
拿回這些權利的研究者，
拿回這些權利的研究者，
09:34
means that going forward, as these tools evolve
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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。
我們是向前發展的。
09:37
and advance,
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09:38
means that we're going to have an educated
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我們將有一組教育程度更高、
更有力的用戶數據，
更有力的用戶數據，
09:40
and empowered user base,
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09:41
and I think all of us can agree
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我相信大家都會認同
09:42
that that's a pretty ideal way to go forward.
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朝此理想的發展方式前進。
09:45
Thank you.
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謝謝。
09:47
(Applause)
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（掌聲）
ABOUT THE SPEAKER
Jennifer Golbeck - Computer scientistAs 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.
Jennifer Golbeck | Speaker | TED.com