TEDxMidAtlantic 2013
Jennifer Golbeck: Your social media "likes" expose more than you think
詹尼弗·高尔拜克: 炸薯条之迷：你以为社交媒体的“赞”就是单纯的“赞”而已吗
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你喜欢吃炸薯条吗？你在面谱网上给过它们“赞”吗？看看这个视频，你会惊奇地发现面谱网（以及其他网站）怎样从你随手的“赞”和“分享”中预测与你相关的信息。计算机科学家詹尼弗·高尔拜克解释这一种做法，解释某些科技应用软件其实并没那么趣致——以及为什么她觉得我们应该把信息的控制权交还给真正的拥有人。
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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如果你还记得网络时代的头十年，
00:14
it was really a static place.
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网络是一个水尽鹅飞的地方。
00:16
You could go online, you could look at pages,
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你可以上网，你可以浏览网页，
00:19
and they were put up either by organizations
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当时的网站
00:21
who had teams to do it
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要么是由某个组织的专门团队建立，
00:23
or by individuals who were really tech-savvy
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要么就是由真正的技术行家所做，
00:25
for the time.
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这就是当时情况。
00:27
And with the rise of social media
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但在二十一世纪初
00:28
and social networks in the early 2000s,
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随着社交媒体以及社交网络的兴起，
00:31
the web was completely changed
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网络发生了翻天覆地的变化：
00:33
to a place where now the vast majority of content
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如今网络上大部分的互动内容
00:36
we interact with is put up by average users,
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都是由大众网络用户提供，
00:40
either in YouTube videos or blog posts
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既有Youtube视频，也有博客文章，
00:42
or product reviews or social media postings.
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既有产品评论，也有社交媒体发布。
00:46
And it's also become a much more interactive place,
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与此同时，互联网成为了一个有更多互动的地方，
00:48
where people are interacting with others,
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人们在这里互相交流、
00:51
they're commenting, they're sharing,
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互相评论、互相分享，
00:52
they're not just reading.
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而不只是阅读信息。
00:54
So Facebook is not the only place you can do this,
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面谱网不是唯一一个你可以做这些事情的地方，
00:56
but it's the biggest,
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但它确实是最大的一个，
00:57
and it serves to illustrate the numbers.
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并且它用数字来证明这点。
00:59
Facebook has 1.2 billion users per month.
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面谱网每个月有12亿用户。
01:02
So half the Earth's Internet population
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由此可见，地球上一半的互联网用户
01:04
is using Facebook.
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都在使用面谱网。
01:06
They are a site, along with others,
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这些都是网站，
01:08
that has allowed people to create an online persona
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允许人们在网上创建不同的角色，
01:11
with very little technical skill,
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但这些人又不需要有多少计算机技能，
01:13
and people responded by putting huge amounts
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而人们的反应是
在网上输入大量的个人信息。
01:15
of personal data online.
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01:17
So the result is that we have behavioral,
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结果是，我们拥有数以亿计人的
01:20
preference, demographic data
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行为信息、喜好信息
01:22
for hundreds of millions of people,
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以及人口数据资料。
01:24
which is unprecedented in history.
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这在历史上前所未有。
01:26
And as a computer scientist,
what this means is that
what this means is that
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对于作为计算机科学家的我来说，这意味着
01:29
I've been able to build models
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我能够建立模型
01:30
that can predict all sorts of hidden attributes
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来预测各种各样的
01:32
for all of you that you don't even know
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你或许完全没有意识到的
01:35
you're sharing information about.
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与你所分享的信息相关的隐藏信息。
01:37
As scientists, we use that to help
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作为科学家，我们利用这些信息
01:39
the way people interact online,
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来帮助人们在网上交流。
01:41
but there's less altruistic applications,
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但也有人用此来谋取自己的私欲，
01:44
and there's a problem in that users don't really
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而问题是，用户并没有真正理解
01:46
understand these techniques and how they work,
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其中用到的技术和技术的应用方式。
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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即便理解了，也不见得他们有话事权。
01:52
So what I want to talk to you about today
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所以，我今天想谈谈
01:53
is some of these things that we're able to do,
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我们能够做的一些事情，
01:56
and then give us some ideas
of how we might go forward
of how we might go forward
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也启发我们
01:59
to move some control back into the hands of users.
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如何改善情况、让话事权回归用户。
02:02
So this is Target, the company.
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这是塔吉特百货公司的商标。
02:03
I didn't just put that logo
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我并不单单把那个商标
02:05
on this poor, pregnant woman's belly.
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放在这个可怜的孕妇的肚子上。
02:07
You may have seen this anecdote that was printed
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或许在福布斯杂志上
02:09
in Forbes magazine where Target
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你看过这么一则趣事：
02:11
sent a flyer to this 15-year-old girl
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塔吉特百货公司给这个15岁女孩寄了一份传单，
02:13
with advertisements and coupons
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传单上都是婴儿奶瓶、尿布、
02:15
for baby bottles and diapers and cribs
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婴儿床的广告和优惠券。
02:17
two weeks before she told her parents
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这一切发生在
02:19
that she was pregnant.
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她把怀孕消息告诉父母的两周前。
02:21
Yeah, the dad was really upset.
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没错，女孩的父亲很生气。
02:24
He said, "How did Target figure out
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他说：”塔吉特是如何
02:25
that this high school girl was pregnant
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在连这个高中女生的父母都尚未知情之前
02:27
before she told her parents?"
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就知道她怀孕了？“
02:29
It turns out that they have the purchase history
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原来，塔吉特有成千上万的顾客，
02:32
for hundreds of thousands of customers
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并拥有他们的购买历史记录，
02:34
and they compute what they
call a pregnancy score,
call a pregnancy score,
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他们用计算机推算出他们所谓的“怀孕分数”，
02:37
which is not just whether or
not a woman's pregnant,
not a woman's pregnant,
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不仅能知道一个女性是否怀孕，
02:39
but what her due date is.
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而且还能计算出她的分娩日期。
02:41
And they compute that
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他们计算出的结果
02:42
not by looking at the obvious things,
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不单单是基于一些显而易见的事情，
02:44
like, she's buying a crib or baby clothes,
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比如说，她准备买个婴儿床或孩子的衣服，
02:46
but things like, she bought more vitamins
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更是基于其他一些事情，
02:49
than she normally had,
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例如她比平时多买了维他命，
02:51
or she bought a handbag
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或她买了一个新的手提包
02:52
that's big enough to hold diapers.
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大得可以放尿布。
02:54
And by themselves, those purchases don't seem
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单独来看这些消费记录
02:56
like they might reveal a lot,
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或许并不能说明什么，
02:59
but it's a pattern of behavior that,
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但这确是一种行为模式，
03:01
when you take it in the context
of thousands of other people,
of thousands of other people,
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当你有大量人口背景作比较，
03:04
starts to actually reveal some insights.
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这种行为模式就开始透露一些见解。
03:06
So that's the kind of thing that we do
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当我们根据社交媒体来预测关于你的一些事情时，
03:08
when we're predicting stuff
about you on social media.
about you on social media.
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这便是我们常做的一类事情。
03:11
We're looking for little
patterns of behavior that,
patterns of behavior that,
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我们着眼于零星的行为模式，
03:14
when you detect them among millions of people,
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当你在众人中发现这些行为模式时，
03:16
lets us find out all kinds of things.
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会帮助我们发现各种各样的事情。
03:19
So in my lab and with colleagues,
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在我的实验室，在同事们的合作下，
03:21
we've developed mechanisms where we can
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我们已经开发了一些机制
03:22
quite accurately predict things
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来较为准确地推测一些事情，
03:24
like your political preference,
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比如你的政治立场、
03:26
your personality score, gender, sexual orientation,
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你的性格得分、性别、性取向、
03:29
religion, age, intelligence,
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宗教信仰、年龄、智商，
03:32
along with things like
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另外还有：
03:34
how much you trust the people you know
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你对认识的人的信任程度、
03:36
and how strong those relationships are.
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你的人际关系程度。
03:38
We can do all of this really well.
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我们能够很好地完成这些推测。
03:39
And again, it doesn't come from what you might
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我在这里在强调一遍，这种推测并基于
03:41
think of as obvious information.
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在你看来显而易见的信息。
03:44
So my favorite example is from this study
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我最喜欢的例子是来自
03:46
that was published this year
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今年发表在
03:47
in the Proceedings of the National Academies.
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美国国家论文集上的一个研究。
03:49
If you Google this, you'll find it.
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你可以在谷歌搜索找到这篇文章。
03:50
It's four pages, easy to read.
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这篇文章总共四页，容易阅读。
他们仅仅研究了人们在面谱网上的“赞”，
03:52
And they looked at just people's Facebook likes,
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03:55
so just the things you like on Facebook,
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也就是你在面谱网上喜欢的事情。
03:57
and used that to predict all these attributes,
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他们利用这些数据来预测
03:59
along with some other ones.
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之前所说的所有特性，还有其他的一些特性。
04:01
And in their paper they listed the five likes
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在文章中列举了
04:04
that were most indicative of high intelligence.
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最能够显示高智商的五个“赞”。
04:07
And among those was liking a page
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在这五项中
04:09
for curly fries. (Laughter)
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赞“炸扭薯”页面的是其中之一
04:11
Curly fries are delicious,
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炸扭薯很好吃，
04:13
but liking them does not necessarily mean
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但喜欢吃炸扭薯
04:15
that you're smarter than the average person.
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并不一定意味着你比一般人聪明。
04:17
So how is it that one of the strongest indicators
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那么为什么喜欢某个页面
04:21
of your intelligence
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就成为显示你智商
04:22
is liking this page
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的重要因素，
04:24
when the content is totally irrelevant
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尽管该页面的内容和所预测的属性
04:26
to the attribute that's being predicted?
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与此毫不相干？
04:28
And it turns out that we have to look at
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事实是我们必须审视
04:30
a whole bunch of underlying theories
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大量的基础理论，
04:32
to see why we're able to do this.
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从而了解我们是如何做到准确推测的。
04:34
One of them is a sociological
theory called homophily,
theory called homophily,
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其中一个基础理论是社会学的同质性理论，
04:37
which basically says people are
friends with people like them.
friends with people like them.
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主要意思是人们和自己相似的人交朋友。
04:40
So if you're smart, you tend to
be friends with smart people,
be friends with smart people,
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所以说，如果你很聪明，你倾向于和聪明的人交朋友。
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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如果你还年轻，你倾向于和年轻人交朋友。
04:45
and this is well established
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这是数百年来
04:46
for hundreds of years.
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公认的理论。
04:48
We also know a lot
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我们很清楚
04:49
about how information spreads through networks.
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信息在网络上传播的传播途径。
04:52
It turns out things like viral videos
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结果是，流行的视频、
04:54
or Facebook likes or other information
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脸书上得到很多“赞”的内容、
04:56
spreads in exactly the same way
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或者其他信息的传播，
04:58
that diseases spread through social networks.
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同疾病在社交网络中蔓延的方式是相同的。
05:01
So this is something we've studied for a long time.
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我们在这方面已经研究很久了。
05:02
We have good models of it.
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我们己经建立了很好的模型。
05:04
And so you can put those things together
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你能够将所有这些事物放在一起，
05:06
and start seeing why things like this happen.
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看看为什么这样的事情会发生。
05:09
So if I were to give you a hypothesis,
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如果要我给你一个假说的话，
05:11
it would be that a smart guy started this page,
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我会猜测一个聪明的人建立了这个页面，
05:14
or maybe one of the first people who liked it
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或者第一个喜欢这个页面的人
05:16
would have scored high on that test.
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拥有挺高的智商得分。
05:18
And they liked it, and their friends saw it,
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他们喜欢了这个页面，然后他们的朋友看到了，
05:20
and by homophily, we know that
he probably had smart friends,
he probably had smart friends,
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根据同质性理论，我们知道这些人可能有聪明的朋友，
05:23
and so it spread to them,
and some of them liked it,
and some of them liked it,
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然后他们看到这类信息，他们中的一部分人也喜欢，
05:26
and they had smart friends,
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他们也有聪明的朋友，
05:28
and so it spread to them,
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所以这类信息也传到其他朋友那里，
05:28
and so it propagated through the network
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所以信息就在网络上
05:30
to a host of smart people,
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在聪明人的圈子里流传开来了，
05:33
so that by the end, the action
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因此到了最后，
05:35
of liking the curly fries page
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喜欢炸扭薯的这个页面
05:37
is indicative of high intelligence,
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就成了高智商的象征，
05:39
not because of the content,
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而不是因为内容本身，
05:41
but because the actual action of liking
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而是“喜欢”这一个实际行动
05:43
reflects back the common attributes
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反映了那些也付诸同样行动的人
05:45
of other people who have done it.
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的相同特征。
05:48
So this is pretty complicated stuff, right?
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听起来很复杂，对吧？
05:51
It's a hard thing to sit down and explain
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对于一般用户来说
05:53
to an average user, and even if you do,
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它比较难解释清楚，就算你解释清楚了，
05:56
what can the average user do about it?
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一般用户又能利用它来干嘛呢？
05:58
How do you know that
you've liked something
you've liked something
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你又怎么能知道你喜欢的事情
06:00
that indicates a trait for you
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反映了你什么特征
06:01
that's totally irrelevant to the
content of what you've liked?
content of what you've liked?
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而且这个特征还和你喜欢的内容毫不相干呢？
06:05
There's a lot of power that users don't have
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用户其实没有太多的能力
06:08
to control how this data is used.
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去控制这些数据的使用。
06:10
And I see that as a real
problem going forward.
problem going forward.
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我把这个看作将来的真实问题，
06:13
So I think there's a couple paths
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我认为，要是我们想让用户拥有
06:15
that we want to look at
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使用这些数据的能力，
06:16
if we want to give users some control
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那么有几条路径
06:18
over how this data is used,
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我们需要探究，
06:20
because it's not always going to be used
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因为这些数据并不总是
06:21
for their benefit.
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用来为他们谋利益。
06:23
An example I often give is that,
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这有一个我经常举的例子，
06:24
if I ever get bored being a professor,
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如果我厌倦了当一名教授，
06:26
I'm going to go start a company
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我会选择自己开家公司
06:28
that predicts all of these attributes
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这家公司能预测这些特性和事物
06:29
and things like how well you work in teams
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例如你在团队里的能力
06:31
and if you're a drug user, if you're an alcoholic.
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例如你是否是一个吸毒者或酗酒者。
06:33
We know how to predict all that.
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我们知道如何去预测这些特性。
06:35
And I'm going to sell reports
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然后我就会把这些报告
06:36
to H.R. companies and big businesses
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卖给那些人力资源公司
06:39
that want to hire you.
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和想要雇佣你的大公司。
06:41
We totally can do that now.
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我们完全可以做到这点。
06:42
I could start that business tomorrow,
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我明天就能开始这个项目，
06:44
and you would have
absolutely no control
absolutely no control
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并且你对我这用使用你的数据
06:46
over me using your data like that.
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是一点办法也没有的。
06:48
That seems to me to be a problem.
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这对我来说是一个问题。
06:50
So one of the paths we can go down
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所以我们可选的其中一条路径
06:52
is the policy and law path.
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是政策和法律这条途径。
06:54
And in some respects, I think
that that would be most effective,
that that would be most effective,
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某程度上我觉得这可能是最有效的
06:57
but the problem is we'd
actually have to do it.
actually have to do it.
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但问题是，事实上我们将不得不这么做。
07:00
Observing our political process in action
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观察我们目前的政治进程
07:03
makes me think it's highly unlikely
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让我觉得在美国
07:05
that we're going to get a bunch of representatives
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把一帮代表们聚在一起
07:07
to sit down, learn about this,
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让他们坐下来理解这个问题，
07:09
and then enact sweeping changes
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然后颁布有关知识产权法方面的颠覆性条例，
07:11
to intellectual property law in the U.S.
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让用户掌控自己的数据，
07:13
so users control their data.
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这似乎是不可能的。
07:16
We could go the policy route,
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我们可以走政策途径，
07:17
where social media companies say,
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这样社交媒体公司就会告诉你，
07:18
you know what? You own your data.
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你知道吗？你的确拥有你的数据。
07:20
You have total control over how it's used.
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你绝对能自己决定要怎么去用。
07:22
The problem is that the revenue models
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但问题在于大部分的社交媒体公司
07:24
for most social media companies
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他们的盈利模式
07:26
rely on sharing or exploiting
users' data in some way.
users' data in some way.
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在某方面取决于分享或挖掘用户的数据资料。
07:30
It's sometimes said of Facebook that the users
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所以有时会说面谱网的用户并不是顾客，
07:32
aren't the customer, they're the product.
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而是产品。
07:34
And so how do you get a company
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那么你要怎样让一个公司
07:37
to cede control of their main asset
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将他们的主要资产控制权
双手拱让给用户呢？
07:39
back to the users?
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07:41
It's possible, but I don't think it's something
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这是可能的，但我不觉得
07:42
that we're going to see change quickly.
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我们能很快见证这种改变。
07:45
So I think the other path
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所以我认为我们得走另一条途径
07:46
that we can go down that's
going to be more effective
going to be more effective
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一条更有效的途径，
07:48
is one of more science.
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一条更加科学的途径。
07:50
It's doing science that allowed us to develop
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这途径是开发一种技术
07:52
all these mechanisms for computing
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让我们能够发展所有这些机制
07:54
this personal data in the first place.
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来首先处理自己的个人信息资料。
07:56
And it's actually very similar research
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而这很接近
07:58
that we'd have to do
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我们必须做的研究，
08:00
if we want to develop mechanisms
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要是我们想要发展这些机制
08:02
that can say to a user,
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跟用户说明，
08:04
"Here's the risk of that action you just took."
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“这样做你需要承担那样的风险。”
08:06
By liking that Facebook page,
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你在面谱网上点“赞”
08:08
or by sharing this piece of personal information,
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或者分享一些私人信息，
08:10
you've now improved my ability
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就相当于增强了我的能力
08:12
to predict whether or not you're using drugs
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去预测你是不是在吸毒
08:14
or whether or not you get
along well in the workplace.
along well in the workplace.
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或者你在工作中是否顺利。
08:17
And that, I think, can affect whether or not
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我觉得，这样做
08:19
people want to share something,
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能够影响人们分享的决定：
08:20
keep it private, or just keep it offline altogether.
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是要保持私隐，还是在网上只字不提。
08:24
We can also look at things like
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我们也可以探究一些别的，例如
08:25
allowing people to encrypt data that they upload,
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让人们去给上传的东西加密，
08:28
so it's kind of invisible and worthless
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那么像面谱网这样的网站
08:30
to sites like Facebook
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或其他能获取信息的第三方来说
08:31
or third party services that access it,
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这些信息就隐秘很多，也少了很多意义，
08:34
but that select users who the person who posted it
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而且只有上传人指定的用户
08:37
want to see it have access to see it.
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才有浏览的权限。
08:40
This is all super exciting research
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从智能的角度来看，
08:42
from an intellectual perspective,
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这是一个非常振奋人心的研究，
08:43
and so scientists are going to be willing to do it.
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而且科学家们也会乐意去做这样的事。
08:45
So that gives us an advantage over the law side.
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这样在法律方面，我们就有优势了。
08:49
One of the problems that people bring up
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当我谈论到这个话题时，
08:51
when I talk about this is, they say,
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人们提到的其中一个问题，就是
08:52
you know, if people start
keeping all this data private,
keeping all this data private,
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如果当人们开始把这些数据进行保密，
08:55
all those methods that you've been developing
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那些你研发的用来预测
08:57
to predict their traits are going to fail.
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人们特性的手段都会作废。
09:00
And I say, absolutely, and for me, that's success,
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我会说，绝对会作废，但对我来说，这是成功，
09:03
because as a scientist,
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因为作为一个科学家，
09:05
my goal is not to infer information about users,
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我的目标不是去推测出用户的信息，
09:09
it's to improve the way people interact online.
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而是提高人们在网上互动的方式。
09:11
And sometimes that involves
inferring things about them,
inferring things about them,
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虽然有时涉及到推测用户的资料，
09:15
but if users don't want me to use that data,
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但如果用户不希望我们用他们的数据，
09:18
I think they should have the right to do that.
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我觉得他们应该有权去拒绝。
09:20
I want users to be informed and consenting
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我希望用户能被告知
09:22
users of the tools that we develop.
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并且赞同我们开发的这种工具。
09:24
And so I think encouraging this kind of science
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所以我认为，鼓励这类科学，
09:27
and supporting researchers
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支持这些研究者们
09:29
who want to cede some of that control back to users
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这些愿意放弃部分控制，退还给用户们，
09:32
and away from the social media companies
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并且不让社交媒体公司接触数据的研究者们
09:34
means that going forward, as these tools evolve
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随着这些工具的进化和提高
09:37
and advance,
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这一切意味着向前的发展，
09:38
means that we're going to have an educated
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意味着我们将会拥有一个
09:40
and empowered user base,
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有素质有权力的用户基础，
09:41
and I think all of us can agree
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我觉得我们都会同意
09:42
that that's a pretty ideal way to go forward.
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这是一个理想的前进目标。
09:45
Thank you.
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谢谢。
09:47
(Applause)
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（掌声）
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