As the Pirates Become ceos: - 麻省理工学院人工智能研究专业

As the Pirates Become ceos:
The Closing of the Open Internet

Zeynep Tufekci

抽象的: The early Internet witnessed the flourishing of a digitally networked public sphere in which many
人们, including dissidents who had little to no access to mass media, found a voice as well as a place to connect
with one another. As the Internet matures, its initial decentralized form has been increasingly replaced by a
small number of ad-½nanced platforms, such as Facebook and Google, which structure the online experience
of billions of people. These platforms often design, 控制, influence, and “optimize” the user experience accord –
ing to their own internal values and priorities, sometimes using emergent methods such as algorithmic ½ltering
and computational inference of private traits from computational social science. The shift to a small number
of controlling platforms stems from a variety of dynamics, including network effects and the attractions of
easier-to-use, closed platforms. This article considers these developments and their consequences for the vital ity
of the public sphere.

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ZEYNEP TUFEKCI is Assistant Pro-
fessor at the School of Information
and Library Science, with an af½liate
appointment in the Department of
Sociology, at the University of North
Carolina, 教堂山. 她是au-
thor of Beautiful Tear Gas: The Ecstatic,
Fragile Politics of Networked Protests in
21世纪 (即将推出 2016)
and coeditor of Inequity in the Technop –
olis: 种族, 班级, 性别, and the Digital
Divide in Austin (with Joseph Straub-
haar, Jeremiah Spence, and Roberta
G. Lentz, 2012). She is also a contrib –
uting opinion writer for The New York
时代.

I traveled to Cairo in the spring of 2011, a few months

after the fall of President Hosni Mubarak. Egypt was
unsettled but jubilant, and the rest of the Middle East
had not yet fallen into war or renewed authoritarian-
主义. One of the Egyptians I interviewed was a blogging
pioneer whom I will call Hani.1 In the early 2000s,
Hani had been among the ½rst to take advantage of the
burst of freedom experienced by Egyptians before the
authorities fully caught on to the Internet’s revolu-
tionary potential. Many bloggers made it through the
Mubarak era largely unscathed because the govern-
ment could not keep up with or fully understand the
new medium. 很遗憾, Hani had caught the at –
tention of the government; he was tried and sentenced
to years in prison for the crime of insulting Mubarak.
Throughout his imprisonment, he remained de½ant.
He was released in November 2010, just months be-
fore a Facebook page would spark a revolution that
would dramatically change the coun try, the region,
and the world.

Before going to jail, Hani felt that his blog had been
a bustling crossroads of discussion. His voice reached

作为
Pirates
Become
CEOs:
The Closing
of the Open
互联网

farther than he had ever thought possible.
After his multiyear involuntary hiatus, 他
resumed blogging, he told me, with enthu-
siasm and excitement. But when he came
out of jail in late 2010, he found that his blog,
and much of the Egyptian blogosphere,
had become a comparative wasteland.

“Where is everybody?” Hani answered

他自己: “They’re on Facebook.”

At the time I interviewed him, that did
not seem like such a bad development. Just
a few months earlier, a Facebook group ti-
tled “We are All Khaled Saed”–named af-
ter a young Egyptian man who had been
tor tured and killed by the police–had be-
come the organizational core of the revolu –
的. The page was created in June 2010, A
few days after Saed’s death became public
知识. 这 (then-anonymous) admin –
istrator of the page was Wael Ghonim, A
Google employee and early adopter of the
Internet in the region. Ghonim had foreseen
Facebook’s potential to reach large num-
bers of ordinary people: in just one month,
his page gathered more than one hundred
thousand readers, and ordinary Egyptians
began using it to engage in political discus-
sion.2 In later interviews, some of those who
participated on the page told me that they
felt jubilant and liberated to be ½nally speak –
ing about politics with other Egyptians.

After the Tunisian revolution of early 2011,
the “We are All Khaled Said” page became
a hotbed of conversation for Egyptians who
longed for a similar upheaval. After much
讨论, including polls asking the page’s
readers what they thought should be done,
Wael Ghonim created an event titled “The
革命,” scheduled for January 25, 2011,
which was already a traditonal day of pro –
test in Egypt.3 Hundreds of thousands of
Egyptians accepted an “evite” to “The Rev –
olution,” displaying their dissent openly,
many perhaps for the ½rst time, to their on –
line social networks.

Emboldened by the outpouring of dissent,
thousands of people assembled in Tahrir

Square on January 25, 2011. One year prior,
only about one hundred protesters met in
Tahrir Square, where they were surrounded
and outnumbered by the police. But this
年, the protest quickly swelled to include
hundreds of thousands of Egyptians who
occupied the square until Mubarak stepped
向下. To many activists I talked with, Face –
book’s reach felt empowering. A survey of
Tahrir protesters con½rmed that social me-
dia had been essential to the early turnout
that had triggered the avalanche of dissent.4
Egyptian use of Facebook continued to grow,
and it became plainly obvious that Face-
book had become a major player in the civ –
ic sphere. Even the new military council that
replaced Mubarak launched a Facebook
页.

But what did it mean for Facebook, a cor –
porate platform, to become so central to the
political life of the country? That was less
清除.

With the advent of social media platforms
in the mid-2000s, the “networked public
sphere”–the burgeoning civic space online5
that had been developed mostly through
blogs–expanded greatly, but with a simul-
taneous shift to commercial spaces.6 Many
scholars and civic activists worried about
how “sovereigns of cyberspace,” as Internet-
freedom advocate, journalist, and author Re –
becca MacKinnon called these online plat –
形式, would wield their power.7 Would
they censor and restrict freedoms to serve
advertisers or governments with whom they
were trying to curry favor? Would they
turn over user information to repressive re –
gimes? MacKinnon was prescient in iden-
tifying the core problem: the growth of pri –
vately owned spaces that functioned as pub –
lic commons. 随着时间的推移, the threats posed
by this relationship may exceed even our
earlier concerns about censorship.

Driven by structural dynamics and cor-
porate motivations, as well as by character –
istics of the Internet, these new social plat-
forms are remaking the Internet in a way

代达罗斯, the Journal ofthe American Academy of Arts & 科学

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that imperils the open architecture of the
early Web that felt so intoxicatingly em-
powering to many of its users. The conse-
quences are profound. This article examines
where we are now, and then briefly traces
the dynamics that have led us here.

在 2015, Hossein Derakhshan–who has

been called the “grandfather” of the Iranian
blogosphere–left prison after serving six
years of a nineteen-year sentence for blog-
ging, including long stretches of solitary
con ½nement. But prison did not break him;
反而, 他说, what nearly broke his heart
was what he found online when he started
blogging again.8

After being released, Derakhshan learned
that he needed to adapt to the new digital
environment and use the new commercial
social networks. Up for innovation and
改变, he created a Facebook account and
posted a link to his blog. To his dismay, 他的
post disappeared after just a few “likes.”
Likes are the main currency in Facebook’s
all-important algorithm that decides which
posts to display to other users, 以及哪个
to hide. In the new world of social media,
posts like Derakhshan’s could disappear
with out being seen by more than a handful
of people. Derakhshan was despondent
about trying to learn the ropes of this new
世界. But he soon realized that his per-
sonal grasp of the platform was not the only
missing ingredient.

The new platforms were strangling access
to the hyperlink, directing users to content
within their walls and regulating access to
the outside Web in very speci½c ways. 骗局 –
tent like his, which was hosted outside of
Facebook’s territory, did not stand a chance.
Derakhshan wrote the essay “The Web
We Have to Save” about his new experience
of being online:

Nearly every social network now treats a link
as just the same as it treats any other object–
the same as a photo, or a piece of text–instead

of seeing it as a way to make that text richer.
You’re encouraged to post one single hyper-
link and expose it to a quasi-democratic pro –
cess of liking and plussing and hearting: Ad –
ding several links to a piece of text is usually
not allowed. Hyperlinks are objectivized, iso –
lated, stripped of their powers.

同时, these social networks tend
to treat native text and pictures–things that
are directly posted to them–with a lot more
respect than those that reside on outside web
页面. . . . A link to the pictures somewhere
outside Facebook . . . are much less visible to
Facebook itself, and therefore get far fewer
likes. The cycle reinforces itself . . . Instagram
–owned by Facebook–doesn’t allow its au-
diences to leave whatsoever. You can put up
a web address alongside your photos, 但它
won’t go anywhere. Lots of people start their
daily online routine in these cul de sacs of
社交媒体, and their journeys end there.9

There are billions of people on the Inter-
网, but a few services capture or shape most
of their activities. Take Facebook: it has 1.5
billion users, a billion of whom log in daily
to see updates and news from the hundreds
of people they have “friended” on the plat-
form.10 Or consider Google: more than one
billion people use the site to run more than
three billion Google searches per day. Face –
book recently announced a program en –
cour aging publishers to upload articles to
Facebook’s servers to make them appear
faster to the end-users. Google is planning
a similar gambit with “instant” articles of
its own. As smartphones continue to claim
an increasingly large share of Internet users,
Google is also designing a new way to dis-
play pages on mobile devices.11 Google’s
new scheme would shift more power to the
company; 尽管, as with all the other tran –
地点, it would offer bene½ts to users as
出色地, which often serve to mask, 或者至少
make palatable, the expansion of power.

For an increasing number of people,
Face book and Google are the Internet, 或

Zeynep
Tufekci

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145 (1) 冬天 2016

作为
Pirates
Become
CEOs:
The Closing
of the Open
互联网

least the framework that shapes their ex-
perience of it.12 These platforms own the
most valuable troves of user data; 控制
the user experience; have the power to de-
cide winners and losers, through small
chang es to their policies and algorithms,
in a variety of categories, 包括新闻,
prod ucts, and books; and use their vast earn –
ings to buy up potential competitors.

I talked with Derakhshan (在线的, 自从他
is still in Iran) about his experiences, shar-
ing my own research about the shift to a
world of algorithmic walled gardens. 两个都
of us are aware that current social media
plat forms reach many more people than
the Internet did in the heydays of blogging.
That is not the problem. Neither is it the
existence of more frivolous or mundane
con tent online; cute cat and baby images
are part of the package. The problem is the
shift in the architecture of the Internet. 在
ways both dramatic and subtle, the shift
has begun to create new profound and far-
reaching problems. In Derakhshan’s words,
a link is not just a link; it is a relationship.
The power of the Internet comes from our
relationships on it. And these relationships
are increasingly mediated by the platforms
that collect data about us; make judgments
about what is relevant, 重要的, and vis-
ible; and seek to shape our experiences for
commercial or political gain.

How did we get here? And how much
pow er is now concentrated in these plat-
形式? The answers to these questions are
connected and offer hints of possible alter-
native futures.

Legal scholar Lawrence Lessig has fa-
mously listed four forces that shape “cyber –
space”: 法律, 规范, 市场, and code.13 He
compared his model to the offline world
where law, 规范, 市场, and architecture
play a major role in shaping society. Lessig
analogized computer code, which de½nes
how online platforms work, to the role ar-
chitecture plays offline. Take the layout of

a city, 例如: When residential and
of½ce buildings are separate, and people live
in far-flung suburbs, there are social, 政治-
伊卡尔, and cultural consequences. Low walk-
ability may contribute to unhealthy life –
styles. Or political polarization may increase
while people segregate by income levels and
种族.

在线的, computer code offers a similar
structuring power. 例如, Facebook
requires mutual consent to interact, 尽管
Twitter allows people to “follow” someone
else without being followed back. On Face –
书, friending someone requires acquies –
cence on both sides: the person making
the request and the person accepting it. 在
推特, any public account can be followed
with just a click, without having to formally
ask for permission. These structures are
formed through decisions made by the peo-
ple who run, administer, and create the code
for these platforms, and are implemented
by in-house coders, resulting in different
social and political environments for each
服务. Facebook tends to have smaller net –
works made up of friends, 家庭, 和交流电-
quaintances, while Twitter is better suited
for fan/celebrity relationships in which the
few can be followed by the many. 在线的
platforms are shaped not only by the code
that structures visibility and access, but by
computation and data as well. This combi-
nation gives online platforms powers for
which there are no simple analogies in the
offline world.

The massive accumulation of user data
has been written about extensively.14 There
is an increasing amount of data about every –
一. More and more social, 政治的, 和
½nancial interactions are performed online.
More and more people carry phones that
connect to the Internet and log their loca-
tion and activities. Everyday objects are in-
creasingly acquiring sensors that collect in –
formation even about passersby. Some of
these data are accessed by governments for
political purposes; some are used by com-

代达罗斯, the Journal ofthe American Academy of Arts & 科学

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panies and advertisers for marketing. Fi-
nancial institutions mine data to check
cred it-worthiness. Occasionally, the data are
leaked, hacked, or otherwise released for
reasons that can range from crime to politics
to mischief. Ordinary people have very lit-
tle idea about who holds what kind of data
about them, or how the data are used. 这
amount of accumulated data and the asym –
metry of power between the people who
are monitored and surveilled and the plat-
forms in which the data are held and mobi-
lized is a signi½cant problem, con½rmed by
polls revealing the public’s great uneasiness
about surveillance.15

然而, the involuntary accretion of
mas sive amounts of data about people is
only the tip of the iceberg. In a networked
社会, computation brings another dimen –
sion of asymmetric power. Through tech-
niques that can be loosely collected under
the heading “computational inference”–
the application of statistical methods, 模组 –
eling, and machine learning to vast troves
of data to make predictions–those who
have gathered these data can infer from
them information that has never even been
disclosed.16

换句话说, aided by computation, 大的
data can now answer questions that have
never been asked about individuals who are
the sources of the data:

The advent of big datasets that contain im-
prints of actual behavior and social network
information–social interactions, conversa-
系统蒸发散, friendship networks, history of reading
and commenting on a variety of platforms
–along with advances in computational tech –
niques means that political campaigns (和
的确, advertisers, corporations and others
with the access to these databases as well as
technical resources) can model individual
vot er preferences and attributes at a high lev el
of precision, and crucially, often without ask –
ing the voter a single direct question. Strik-
英利, the results of such models may match
the quality of the answers that were only ex-

tractable via direct questions, and far exceed
the scope of information that could be gath-
ered about a voter via traditional methods.17

Zeynep
Tufekci

The computational inference generated
by machine learning takes place during the
pro cess of sifting through many varieties of
da ta, with the proviso that the data are deep
and rich enough. Inferring political vari-
ables about a person does not require their
participation in overtly political websites or
conversations. 例如, Facebook op-
erates mainly through likes: a one-click op –
eration that signals a user’s approval of a
页, update, or person. The collection of
these likes can be used to model, with sur-
prisingly high statistical reliability, a range
结果的, including “sexual orientation,
种族, religious and political views, 每 –
sonality traits, 智力, 幸福, 使用
of addictive substances, parental separation,
年龄, and gender.”18

This type of analytic power can go be-
yond many of the traditional categories
used by demographers and advertisers to
pro½le the public. By using only their social
media imprints (再次, not directly asking
questions of individuals), 研究人员有
been able to identify people who are likely
to become clinically depressed in the fu-
真实, even before the onset of clinical symp-
toms.19 Much of this research is done with
the best of intentions: 例如, as ear-
ly intervention for new mothers at risk for
postpartum depression.20 However, 这是
easy to see the downsides of making infer-
ences using data in this fashion. Advertisers,
例如, discovered that when women
feel “lonely, 胖的, and depressed” they are
more likely to purchase makeup, 然后
such women are ideal targets for “beauty
interventions.”21 In other words, 女性
who are depressed and lonely can be more
easily sold makeup. It does not take much
imagination to see that advertisers will
there fore want to use data gathered by on-
line platforms to ½nd out exactly who is feel –
ing “lonely, 胖的, and depressed” and mar-

145 (1) 冬天 2016

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作为
Pirates
Become
CEOs:
The Closing
of the Open
互联网

ket to these targeted women at exactly these
次.

The increasing use of opaque computa-
tional methods known as machine learn-
ing–or “neural networks”–adds another
layer of complexity to predictions made
with big data sets. These are systems that
“learn to learn” how to classify individuals
(or whatever type of cases they are present –
ed with) into various categories. 机器-
learning systems are often provided with a
“training set”: a database in which cases
are marked with the correct answers.

例如, to train a machine-learning
系统, an employer might provide it with
a list of employees he has classi½ed as either
“high-performance” or “unsatisfactory,”
ac companied by social-media data about
all employees in the database. Without re-
ceiving direct instruction or a recipe about
what makes a worker either high-perfor –
mance or unsatisfactory, the system learns
the set of associations that are linked to each
outcome, and how to use that knowledge
to classify new employees. On the surface,
this looks a lot like many other methods
that employers use to discriminate among
potential hires. But there is a twist: a ma-
chine-learning system often does not pro-
vide any human-understandable clues to
why it classi½es the way it does. 实际上, 如果
we knew exactly what it was doing, 那里
would be no need for the “machine-learn-
ing” part: we could just program the criteria
ourselves. 事实上, 尽管, all that a man –
ager might know is that the system places
potential hires into one category or the oth –
是, without any understanding of what parts
of the social media big data set were used
as signals for a particular outcome.

For all a hiring manager knows, such a
sys tem might classify applicants based on
criteria such as statistical likelihood of ex-
periencing depression in the future (即使
undiagnosed at the time of evaluation) 或者
the possibility of impending parenthood.
It is well known that current hiring systems

incorporate many biases. 然而, 要是我们
use social media data churned through com –
putational methods for hiring, 我们可能
move from imperfect hiring systems that
we know discriminate against women, 为了
例子, to ones whose workings are hid-
den from us, but nonetheless still discrimi-
nate. This could mean using systems that
discriminate only against women who are
statistically likely to become pregnant soon.
This type of discrimination would not be
visible to employers because neither the
women being hired nor the women not be-
ing hired would be pregnant at the time of
the hiring, and because a machine-learning
system does not display decision-making
variables that are easily interpretable, 甚至
by its engineers. Social media platforms in –
creasingly hold the kind of data that can be
used in these ways.

While this combination of big data and

computation obviously creates signi½cant
挑战, there are additional, equally
daunt ing issues. When combined with the
power of “code” as architecture, in the sense
½rst identi½ed by Lessig,22 platforms can al-
so nudge behavior, quietly and impercept –
ibly, and sometimes in ways that are not di-
rectly visible even to the people who run the
platforms. Facebook, 例如, uses an
algorithm to order the news feed that shows
它是 1.5 billion users’ status updates. 这些
may range from updates that are purely per –
sonal in nature to news articles. Increasing –
莱, for many population segments ranging
from younger people in developed coun-
tries to populations just coming online in
poorer countries, Facebook has become the
number one source of news.23 In poorer
国家, many people are not even aware
that there is an Internet outside of Face-
书,24 and many others choose to stay
completely within Facebook’s realm.25 As
David Clark explains in his essay in this issue,
Facebook has helped ensure this through
promotion of its stripped-down Facebook

代达罗斯, the Journal ofthe American Academy of Arts & 科学

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app–0.facebook.com–which, in agree-
ment with mobile service providers in many
developing countries, does not incur data
charges for users.26

在我的研究中, I have encountered many
people whose Internet routine resembles
下列: If on a desktop computer, A
user launches a browser and types “Face-
book” into Google’s search box, likely un-
aware that the url bar at the top of the
browser is a separate and faster way to get
那里. Google brings up Facebook as the ½rst
link, and the user clicks on Facebook and
proceeds to interact mostly within the site.
If using a mobile platform, which is increas –
ingly the norm, a user will simply launch
the Facebook app and rarely encounter the
open Web at all.

This tendency to stay within Facebook is
what gives Facebook’s architectural deci-
sions such power, and invisibly so. In one
学习, 62.5 percent of users had no idea that
the algorithm controlling their feed exist-
编辑, let alone how it worked.27 This study
used a small sample in the United States,
where the subjects were likely more educat-
ed about the Internet than many other popu –
lations globally, creating a potentially un-
representatively low estimate. The news
feed is a world with its own laws of phys –
集成电路, and the deities that rule it are Facebook
programmers. In this world, some types of
information are nudged and helped to
spread more, while others are discouraged.
There is great power in what we do (and do
不是) see from our friends and acquaintanc –
英语, and increasingly, this is greatly influ-
enced by platform design and code.

Facebook’s own research has shown the
power of its designers’ architectural choic-
英语. In one Facebook experiment, randomly
selected users received a neutral message to
“go vote,” while others, also randomly se-
lected, saw a slightly more social version of
the encouragement, noting also which of
their friends voted using small thumbnails
of their pro½le photos. Matched with voter

rolls, that single message caused 340,000
additional people to turn out to vote in the
2010 我们. congressional elections.28 In an-
other experiment, Facebook randomly se-
lected whether users saw posts with slightly
more upbeat words versus more downbeat
那些: the result was correspondingly slight –
ly more upbeat or downbeat posts by those
same users. Dubbed the “emotional conta –
gion” study, this incident sparked interna-
tional interest in Facebook’s power to shape
the user’s experience.29

The power to shape experience (or per-
haps elections) is not limited to Facebook;
there are other powerful platforms. 对于前-
充足, Google rankings are hugely conse-
quential. A politician can be greatly helped
or greatly hurt if Google chooses to highlight
or hide, 说, a link to a corruption scandal
on the ½rst page of its results. A recent study
showed that slight changes to search rank-
ings can shift the voting preferences of un-
decided voters, and that these shifts can be
hidden so that people show no awareness
of the manipulation.30

For a small taste of how platform choices
affect the civic sphere, consider the case of
the protests in Ferguson, Missouri, in Au-
gust 2014. What started as a community
shaken over the police killing of a young
man under murky circumstances grew into
major protests after the police responded
to initial small-scale–and completely non –
暴力, according to journalists on the scene
–protests by residents with overwhelming
力量, including the use of attack dogs and
tear gas. A few national journalists, 也
ordinary citizens with smartphones, start-
ed tweeting from the scene of the initial pro –
测试. The burgeoning unrest and conflict
soon grew into major Twitter discussions
that later sparked the attention of the main –
stream news media. About three million
tweets were sent before the mass media be –
gan covering events in Ferguson. The na-
tionwide movement that grew from these
events is often referred to as the “Black Lives

Zeynep
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145 (1) 冬天 2016

作为
Pirates
Become
CEOs:
The Closing
of the Open
互联网

Matter” movement, named after the Twit-
ter hashtag.

然而, on the ½rst night of the pro –
测试, the topic was mostly invisible on Face –
book’s algorithmically controlled news
feed.31 Instead, the “ice bucket challenge,”
in which people poured cold buckets of wa –
ter over their heads and, in some cases,
donated to an als charity, dominated the
Face book news feed. This was not a situa-
tion that reflected Facebook users’ lack of
interest in the Ferguson protests; 相当, 它
was an indication that it is hard to “like”
–Facebook’s dominant algorithmic signal
–such disturbing news, while it is easy to
give a thumbs-up to a charity drive. Once a
topic is buried by an algorithm, this be-
comes a self-feeding cycle: fewer people
are able to see it in the ½rst place, with few-
er still choos ing to share it further, causing
the algorithm to bury it deeper. On Twit-
ter’s platform, in which users see all posts
from the people they follow in chronologi-
cal order, the topic grew to dominate dis-
坐垫, trending locally, nationally, 和
glob ally, catching attention of journalists
and broader publics. On Facebook, it barely
surfaced. Given the importance of online
platforms and public attention to political
movements, burying such news is highly
consequential.32 Had our media been exclu –
sively controlled by an algorithm in which
“liking” were the main emotive input, 这
long and hard national conversation about
race and policing in America that was gen-
erated by the Fergu son protests might have
never transformed into a national move-
ment.33
How did we get here? Was it inevitable?

Tracing this path requires combining and
probing the two questions posed by Hani
and Derakhshan, two people who blogged
under repressive regimes and who were re-
leased from prison ½ve years apart. Why is
everyone on Facebook now? And why are
these platforms killing the hyperlink as an

independent relationship between people?
Why are they dictating who sees what?

Some aspects of the answer are decep-
tively simple, and at the same time deeply
结构性的. The open Internet that held so
much generative power took a turn toward
ad-½nanced platforms, while the dangers
lurking for ordinary users from the Inter-
net’s open and trusting design were not
counteracted, causing people to flee to safer
and more user-friendly platforms. 在通讯中-
bination, these two developments encour-
aged, enabled, and forced the creation of
mas sive, quasi-monopolistic platforms,
while incentivizing the platforms to use
their massive troves of data with the power
of computational inference to become bet –
ter spy machines, geared toward ad delivery,
the source of their ½nancing.

From Wikipedia to question-and-answer
sites to countless numbers of sites and blogs
that provide a public service (but not pay-
ment for their creators), the Internet offers
direct proof that people enjoy sharing their
creative and personal output with others.34
If there were ever a need to expand our con –
ception of humanity beyond the restricted
“homo economicus” who works only for
his or her bene½t, the explosion of user-
generated content on the Internet has pro-
vided major evidence.35 However, creative
and altruistic output alone does not provide
½nancing for servers, coders, and database
管理. As the public Internet scaled
up and grew in numbers of participants,
many websites faced a dilemma: 无论
to charge their users, or to sell users’ eye-
balls to advertisers.

It was a crucial turning point: were people
going to be the customers, or were they go-
ing to be the product sold? Almost all of the
major platforms went with advertising. 作为
伊桑·祖克曼, then a staff member of
one of the Internet’s earliest user-generated
platforms, tripod.com, explains:

Advertising became the default business mod –
el on the web, “the entire economic founda-

代达罗斯, the Journal ofthe American Academy of Arts & 科学

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tion of our industry,” because it was the eas-
iest model for a web startup to implement,
and the easiest to market to investors. Web
startups could contract their revenue growth
to an ad network and focus on building an
观众. If revenues were insuf½cient to cov-
er the costs of providing the content or serv-
冰, it didn’t matter–what mattered was au-
dience growth, as a site with tens of millions
of loyal users would surely ½nd a way to gen-
erate revenue.36

These decisions were made partly out of
idealism: a free website-hosting platform
like Tripod also allowed Thai dissidents to
circulate otherwise censored content with –
out worrying about paying for the site. 它
made more sense at the time to have ads
than to charge users. But once advertising
became the way to make money, almost
every thing flowed from it, especially when
combined with another key feature of on-
line platforms: network effects.

Network effects, also called network ex-
ternalities, are the tendency of the value of
some products or services to increase as
more people use them, and to become less
worthwhile when they are not used by oth-
呃, even if the less popular product or ser –
vice is objectively better, cheaper, faster, 或者
more diverse in its offerings. For many on-
line applications, everyone wants to be
where everyone else is. This dynamic al-
lows many online platforms that manage
to get ahead of their competition to com-
pletely dominate their niche:

The more people own fax machines, 对于前任-
充足, the more useful each one becomes.
That is also why there is a single standard for
fax machines–would you switch to a brand
新的, faster fax machine standard if there
was nobody else you could fax with your ma-
中国? Research shows that the presence of
network externalities trumps product pref-
erence or quality; many people will chose a
service that has more users compared to the
one that is otherwise better for them. 这样的

platforms, such as Facebook, tend to quickly
dominate their market and become near-
monopolies. This is also why everyone lists
their wares on Ebay, where all the buyers are,
and advertises on Google, where all the eye-
balls go. The fact that a lot of people already
have Facebook accounts means that consid-
erations of network externalities will result
in existing people staying put, or new people
joining in anyway, even if they have qualms
about the privacy issues.37

While network externalities made it pos –
sible for platforms to become very large,
the ad-½nancing model meant that a mid-
sized platform, even one with hundreds of
millions of users, faced great challenges,
since ads on the Internet are not worth
much.38 An ad-dependent platform can on –
ly survive if it serves enormous numbers
of people. 例如, Wall Street’s in-
vestors have soured on Twitter because it
only has about three hundred million us –
呃. For most products, hundreds of mil-
lions of users would appear to be a huge suc –
过程. In an ad-½nanced online world, that’s
barely enough to get by.

But there is one key path for online ads
to become more valuable for platforms. 如果
platforms accumulate a great amount of
data on their users, and harness computa-
tional inference to “understand” them on
behalf of their advertisers, then the ads,
which have a higher chance of leading to a
购买, are worth a lot more. These adver –
tisers could include both corporate entities
selling products and political campaigns
mar keting politicians. Platforms can also
use their architectural power to create an
environment that is more advertiser-friend –
莱. Until quite recently, 例如, Face-
book allowed likes as the only signal (aside
from making comments) that users could
send about a page or status update. 尽管
Facebook recently expanded choices in a
few countries to include a few more “one-
click” options such as “like,” “love,” “ha-
ha,” “yay,” “wow,” “sad,” and “angry,“ 这

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145 (1) 冬天 2016

作为
Pirates
Become
CEOs:
The Closing
of the Open
互联网

expanded list is still heavily geared toward
positivity, with only two that are typically
associated with negativity: angry and sad.

全面的, many of the issues identi½ed in
this article are a direct consequence of this
combination: Internet platforms are ½ –
nanced by ads that demand great scale, 和
they are fueled by network effects that al-
low such scale through the emergence of
monopolies. These quasi monopolies then
have incentives to collect and process vast
amounts of data on their users to make the
ads more effective for the advertisers, 尽管
also controlling the experience of the users
to keep the platform advertising-friendly,
and to keep the user from leaving the plat-
形式.

The other major development over the

past decade from the user side has been the
lack of attention and resources to ensure
that the open Web–the one in which the
hy perlink and address bar, rather than a
closed platform and its algorithmic and ar-
chitectural choices, dominate navigation–
remains a secure and navigable place for or –
dinary users.

Many of the early protocols that de½ned
the Internet were developed for use by a
trusting, 小的, and closed community of
academic and military research staff. 如何 –
曾经, on the current scale of billions of peo-
普莱, the Internet’s insecurity, and the pro-
liferation of malware, 垃圾邮件, and untrust-
worthy sites, has caused many to retreat to
easier-to-use, relatively safe platforms. 这
ad-½nancing model means that almost all
commercial websites have installed exten-
sive ad-tracking software on their sites,
which is not distinguishable, in effects or
手术, from malware dedicated to spy-
英. Navigating the ordinary, open Internet
now seems treacherous and feels slow (自从
the sites are loaded with ads and tracking
软件).

在 2014, 例如, a massive vulnera-
bility was found in “Openssl,” one of the

protocols that underpins almost all Internet
商业. The bug “heartbleed” allowed
an attacker to read parts of a comput er’s
memory that the program should not ordi-
narily have access to, and to learn crucial
private information, including stored pass –
字. While it is almost too ridiculous to
believe, the Openssl architecture, used by
about two-thirds of all web pages, 包括-
ing almost all major banks, is maintained
by a group of only a dozen people, all but
one of whom are volunteers.39 The crisis
with Openssl was but one example of crit –
ical parts of the Internet’s infrastructure
that provide security for ordinary users be-
ing tended by almost nobody. There is very
little energy or resources dedicated to tend –
ing the commons of the Internet, 和
resulting environment has made ordinary
Web navigation increasingly dif½cult and
user data increasingly insecure. For regular
用户, remaining within trusted walled gar –
dens, like Facebook or Google’s new pro-
posed Web architecture, is a reasonable
选择. This is exactly the scenario warned
against by scholars.

This shift toward the walled gardens is
only increasing as the next billions come
在线的: people with less technical literacy,
less powerful devices, shakier Internet con –
连接, and often mobile-only access. 在
developing nations, the walled gardens of
huge online platforms have many draws.
Network effects means that their expatriate
relatives and friends are most likely to be
on the biggest platforms. A controlled en-
vironment makes the Internet more navi-
gable. Bigger platforms offer better trans-
lation and localization services, something
volunteer sites have more dif½culty provid –
英. Google helps order the chaotic, seem –
ingly endless, choices effectively, while Face –
book offers a way to manage the flow of in-
formation from a user’s social networks,
albeit algorithmically curated within an ad-
delivery platform. And thus, 互联网
giants continue to grow, and have become

代达罗斯, the Journal ofthe American Academy of Arts & 科学

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the dominant landscape of the Internet for
most people.

In his prescient book The Future of the In-

ternet–And How to Stop It, Jonathan Zittrain
warned about these problems, and predict –
ed that unless addressed, they would lead
to the collapse of the open, generative In-
ternet in favor of closed systems.40 Legal
scholar Tim Wu looked into past informa-
tion systems and pointed out that many be –
came dominated by monopolies.41 As early
作为 2003, Deborah L. Spar–now president
of Barnard College–predicted that insur-
gent technologies would pass from the “pi –
rates” that use technologies to disrupt order
to the hands of powerful commercial and
governmental bodies who use it to consol-
idate power.42 The Internet, in some ways,
seems set on this path, although we have
not yet passed the point of no return.

Because ad-based ½nancing quickly de-
volves into large-scale, monopolistic sys-
tems working on behalf of advertisers, 到
change directions, we ½rst must change how
we ½nance the Internet’s platforms, 包括 –
ing ½nancing potential challengers to cur –
rent ly dominant platforms. Alternative
mod els of ½nancing were developed in the
互联网的早期, but these were
quashed, in part because they may have been
too early for mass adoption, but also be-
cause banks and websites resist ed their im –
plementation. 第二, the Inter net’s com-
mons needs tending, which will also require
substantial resources and ½ nancing as well.
A global system whose security depends so
much on volunteer work will, inevitably, 是 –
come a dif½cult-to-navigate, insecure, 和
unpleasant experience, and will result in
peo ple retreating to safer platforms that
cushion the user experience while also con –
trolling it. 第三, we must rec ognize that due
to network effects, unregulated markets
(one of the mechanisms of Lessig’s original
four forces) do not work well on the Internet
for certain kinds of plat forms, 包括

many of the current tech giants. The influ-
ence of network effects is especially power –
ful for user-generated plat forms, since what
partially powers them is user investment.
People have spent a lot of time and effort
building up their positive feedback on eBay
and cultivating their social networks on
Face book. It is unlikely that competition
alone–even competition offered with bet-
ter terms and services–can dislodge these
powerful platforms, given the costs sunk
into them by their users.

The path toward change is uphill, 但
the ½rst step requires the public recognition
of what dissidents in repressive regimes–
often the canaries in mines–have already
discovered: the power of the Internet de-
rives from our ability to freely connect with
each other. These developments are not
changing only from one type of program
or site to another; they are shifting to a new
regime in which our relationships are me-
diated by forces trying to mine our data,
mostly in order to sell a few more ads slight ly
more effectively, but also open to a host of
other political uses.43 From politics to cul-
真实, much power resides with owners of
数据, especially those possessing command
of computation and online architecture.

It is not too late to change this path, 但
to do so requires an open-eyed and realistic
look at the forces that have brought us here
–½nancing models, the need for tending
the security of the Internet’s commons, 的-
mand for usability, and the shift to mobile–
and asking how to generate an alternative
model that can scale-up. That demand still
存在: the ½rst billion Internet users have
experienced, and remember, the admittedly
chaotic early Internet, built upon the energy
and euphoria of people discovering both
in formation and each other. Now that the
Internet is approaching three billion users,
the question facing us is whether their
Inter net experience will much differ from
a tightly regulated coffeehouse within a gi-
gantic shopping mall.

Zeynep
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145 (1) 冬天 2016

作为
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CEOs:
The Closing
of the Open
互联网

尾注
1 He did not ask me to keep his identity secret, but I am not using his name on principle, to avoid
my arguments getting tangled with his views as a result of Google searches run by clumsy repres –
sive regimes.

2 Jennifer Preston, “Movement Began With Outrage and a Facebook Page That Gave It an Outlet,”
纽约时报, 二月 5, 2011, http://www.nytimes.com/2011/02/06/world/middleeast/
06face.html.

3 Wael Ghonim, 革命 2.0: The Power of the People is Greater Than the People in Power–A Memoir
(波士顿: Houghton Mifflin Harcourt, 2012); and the author’s private conversation with Wael
Ghonim (2015).

4 Zeynep Tufekci and Christopher Wilson, “Social Media and the Decision to Participate in Politi –
cal Protest: Observations From Tahrir Square,” Journal of Communication 62 (2) (2012): 363–379,
http://doi.org/10.1111/j.1460-2466.2012.01629.x.

5 Yochai Benkler, The Wealth of Networks: How Social Production Transforms Markets and Freedom

(新天堂, 康涅狄格州: 耶鲁大学出版社, 2007).

6 Steven Johnson, “Can Anything Take Down the Facebook Juggernaut?” Wired, 可能 16, 2012,

http://www.wired.com/2012/05/mf_facebook/.

7 Rebecca MacKinnon, Consent of the Networked: The Worldwide Struggle for Internet Freedom (新的

约克: 基础书籍, 2012).

8 Hossein Derakhshan, “The Web We Have to Save: The Rich, Diverse, Free Web that I Loved – and
Spent Years in an Iranian Jail for – is Dying. Why is Nobody Stopping It?” July 2014, https://
medium.com/matter/the-web-we-have-to-save-2eb1fe15a426.

9 同上.
10 Don Clark and Robert McMillan, “Facebook, Amazon and Other Tech Giants Tighten Grip on
Internet Economy,“ 华尔街日报, 十一月 5, 2015, http://www.wsj.com/articles/
giants-tighten-grip-on-internet-economy-1446771732.

11 Joshua Benton, “Get amp’d: Here’s What Publishers Need to Know about Google’s New Plan
to Speed Up Your Website,” Nieman Lab, 十月 7, 2015, http://www.niemanlab.org/2015/
10/get-ampd-heres-what-publishers-need-to-know-about-googles-new-plan-to-speed-up
-your-website/.

12 Leo Mirani, “Millions of Facebook Users have No Idea They’re Using the Internet,” Quartz,
二月 9, 2015, http://qz.com/333313/milliions-of-facebook-users-have-no-idea-theyre-using
-the-internet/.

13 Lawrence Lessig, Code: And Other Laws of Cyberspace, Version 2.0 (纽约: 基础书籍, 2006).
14 看, 例如, Viktor Mayer-Schönberger and Kenneth Cukier, Big Data: A Revolution that

Will Transform How We Live, 工作, and Think (波士顿: Houghton Mifflin Harcourt, 2013).

15 George Gao, “What Americans Think about nsa Surveillance, National Security and Privacy,”
皮尤研究中心, 可能 29, 2015, http://www.pewresearch.org/fact-tank/2015/05/29/what
-americans-think-about-nsa-surveillance-national-security-and-privacy/.

16 Zeynep Tufekci, “Engineering the Public: Big Data, Surveillance and Computational Politics,”

First Monday 19 (7) (2014), http://dx.doi.org/10.5210/fm.v19i7.4901.

17 同上.
18 Michal Kosinski, David Stillwell, and Thore Graepel, “Private Traits and Attributes are Predic –
table from Digital Records of Human Behavior,” 美国国家科学院院刊
110 (15) (2013): 5802–5805, http://doi.org/10.1073/pnas.1218772110.

19 Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz, “Predicting Depres –
sion via Social Media,” in Proceedings of the Seventh International AAAI Conference on Weblogs and

代达罗斯, the Journal ofthe American Academy of Arts & 科学

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Social Media (帕洛阿尔托, 加利福尼亚州。: Association for the Advancement of Arti½cial Intelligence, 2013),
http://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/viewFile/6124/6351.

Zeynep
Tufekci

20 Munmun De Choudhury, Scott Counts, Eric Horvitz, and Aaron Hoff, “Characterizing and
Predicting Postpartum Depression from Shared Facebook Data,” in Proceedings of the 17th ACM
Conference on Computer Supported Cooperative Work & Social Computing (纽约: 协会
for Computing Machinery, 2014), 626–638, http://doi.org/10.1145/2531602.2531675.

21 Lucia Moses, “Marketers Should Take Note of When Women Feel Least Attractive,” AdWeek,
十月 2, 2013, http://www.adweek.com/news/advertising-branding/marketers-should-take
-note-when-women-feel-least-attractive-152753.
22 Lessig, Code: And Other Laws of Cyberspace, Version 2.0.
23 Amy Mitchell, Jeffrey Gottfried, and Katerina Eva Matsa, “Millennials and Political News,”
皮尤研究中心, 六月 1, 2015, http://www.journalism.org/2015/06/01/millennials-political
-news/.

24 Mirani, “Millions of Facebook Users have No Idea They’re Using the Internet.”
25 World Wide Web Foundation, Women’s Rights Online: Translating Access into Empowerment (Wash –
因顿, 华盛顿特区: World Wide Web Foundation, 2015), http://webfoundation.org/wp-content/
uploads/2015/10/WomensRightsOnlineWF_Oct2015.pdf.

26 参见大卫·D. 克拉克, “The Contingent Internet,代达罗斯 145 (1) (冬天 2016), 9–17.
27 Motahhare Eslami, Aimee Rickman, Kristen Vaccaro, Amirhossein Aleyasen, Andy Vuong, Kar rie
Karahalios, Kevin Hamilton, and Christian Sandvig, “‘I Always Assumed That I Wasn’t Really That
Close to [她]’: Reasoning about Invisible Algorithms in the News Feed,” in Proceedings of the 33rd
Annual ACM Conference on Human Factors in Computing Systems (纽约: Association for Com –
puting Machinery, 2015), 153–162, http://www.researchgate.net/pro½le/Kevin_Hamilton/
publication/275353888__I_always_assumed_that_I_wasn’t_really_that_close_to_her___
Reasoning_about_Invisible_Algorithms_in_News_Feeds/links/553aa2fd0cf245bdd764475f.pdf.
28 Robert M. Bond, 克里斯托弗·J. Fariss, Jason J. 琼斯, Adam D. 我. 克莱默, Cameron Marlow,
Jaime E. Settle, and James H. Fowler, “A 61-Million-Person Experiment in Social Influence and
Political Mobilization,“ 自然 489 (7415) (2012): 295–298, http://doi.org/10.1038/nature11421;
and Jonathan Zittrain, “Facebook Could Decide an Election Without Anyone Ever Finding Out,”
The New Republic, 六月 1, 2014, https://newrepublic.com/article/117878/information-½duciary
-solution-facebook-digital-gerrymandering.

29 Lorenzo Coviello, Yunkyu Sohn, Adam D. 我. 克莱默, Cameron Marlow, Massimo Franceschetti,
Nicholas A. Christakis, and James H. Fowler, “Detecting Emotional Contagion in Massive So-
cial Networks,” PLoS ONE 9 (3) (2014): e90315, http://doi.org/10.1371/journal.pone.0090315.
30 Robert Epstein and Ronald E. 罗伯逊, “The Search Engine Manipulation Effect (seme)
and Its Possible Impact on the Outcomes of Elections,” Proceedings of the National Academy of
科学 112 (33) (2015): E4512–E4521, http://doi.org/10.1073/pnas.1419828112.

31 Zeynep Tufekci, “The Medium and the Movement: Digital Tools, Social Movement Politics, 和
the End of the Free Rider Problem,” Policy & 互联网 6 (2) (2014): 202–208, http://doi.org/10
.1002/1944-2866.POI362.

32 Zeynep Tufekci, “Algorithmic Harms beyond Facebook and Google: Emergent Challenges of
Computational Agency,” Colorado Technology Law Journal [formerly Journal on Telecommunications
and High Technology Law] 13 (2) (2015): 203–218; and Zeynep Tufekci and Deen Freelon, “在-
troduction to the Special Issue on New Media and Social Unrest,” American Behavioural Scientist
57 (7) (2013): 843–847.

33 Tufekci, “Algorithmic Harms beyond Facebook and Google.”
34 Benkler, The Wealth of Networks.
35 Yochai Benkler, The Penguin and the Leviathan: How Cooperation Triumphs over Self-Interest (新的

约克: Crown Publishing Group, 2011).

145 (1) 冬天 2016

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作为
Pirates
Become
CEOs:
The Closing
of the Open
互联网

36 伊桑·祖克曼, “The Internet’s Original Sin,“ 大西洋组织, 八月 14, 2014, http://万维网
.theatlantic.com/technology/archive/2014/08/advertising-is-the-internets-original-sin/
376041/.

37 Zeynep Tufekci, “Facebook, Network Externalities, Regulation,” Technosociology, 可能 26,

2010, http://technosociology.org/?p=137.

38 Zeynep Tufekci, “Mark Zuckerberg, Let Me Pay for Facebook,“ 纽约时报, 六月 4,
2015, http://www.nytimes.com/2015/06/04/opinion/zeynep-tufekci-mark-zuckerberg-let-me
-pay-for-facebook.html.

39 Dan Goodin, “Critical Crypto Bug in Openssl Opens Two-Thirds of the Web to Eavesdropping,”
Ars Technica, 四月 7, 2014, http://arstechnica.com/security/2014/04/critical-crypto-bug-in
-openssl-opens-two-thirds-of-the-web-to-eavesdropping/; and Jose Pagliery, “Your Internet Se-
curity Relies on a Few Volunteers,” cnn Money, 四月 18, 2014, http://money.cnn.com/2014/
04/18/technology/security/heartbleed-volunteers/index.html.

40 See Jonathan Zittrain, The Future of the Internet–And How to Stop It (新天堂, 康涅狄格州: Yale Uni –

大学出版社, 2008).

41 Tim Wu, The Master Switch: The Rise and Fall of Information Empires (纽约: Knopf Doubleday

Publishing Group, 2010).

42 Debora L. Spar, Ruling the Waves: From the Compass to the Internet, a History of Business and Politics

along the Technological Frontier (纽约: Mariner Books, 2003).

43 Tufekci, “Engineering the Public”; and Zittrain, “Facebook Could Decide an Election Without

Any one Ever Finding Out.”

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代达罗斯, the Journal ofthe American Academy of Arts & 科学
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