A Networked Self. Routledge

Edited by Zizi Papacharissi

 Identity. Community and Culture on Social Network Sites

A Networked Self examines self presentation and social connection in the digital
age. This collection brings together new theory and research on online social
networks by leading scholars from a variety of disciplines. Topics addressed
include self presentation, behavioral norms, patterns and routines, social
impact, privacy, class/gender/race divides, taste cultures online, uses of social
networking sites within organizations, activism, civic engagement and political impact.

Zizi Papacharissi is Professor and Head of the Communication Department
at the University of Illinois-Chicago. She is author of A Private Sphere: Democracy
in the Digital Age and editor of Journalism and Citizenship: New Agendas, also
published by Routledge.

A Networked Self
Identity. Community and Culture on Social Network Sites

About the Editor
Zizi Papacharissi (PhD, University of Texas at Austin, 2000), is Professor and Head
of the Communication Department at the University of Illinois-Chicago.
Her work focuses on the social and political consequences of online media. Her book A Private Sphere: Democracy in a Digital Age (Polity Press, 2010) discusses how online media
redefine our understanding of public and private in late-modern democracies. 
She has published three books and over 40 journal articles, book chapters, or reviews.

Acknowledgments
This edited volume is the result of encouragement, trust, and inspiration from
a variety of colleagues, several of whom are also contributors to the volume.
The editor would like to thank Steve Jones for his suggestions and support. I
also appreciate the encouragement provided by my editor, Matthew Byrnie, to
move forward with a proposal on a volume on social network sites. The day-long
conference that brought contributors to A Networked Self together, and
was hosted by the Department of Communication at the University of Illinois-Chicago,
would not have been possible without the generous endorsement of
the College of Liberal Arts and Sciences, and our Dean Dr. Dwight McBride,
and I thank him warmly for his faith in my vision. My research assistant Kelly
Quinn helped preserve sanity at the various planning stages of the volume and
conference, with her knack for planning, insight and thoughtful interventions.
Doctoral candidates at the University of Illinois-Chicago Maggie Griffith and
Gordon Carlson deserve thanks for their help with organizing and chairing sessions
for the Networked Self conference. My colleagues and students at the
University of Illinois-Chicago and Temple University make my everyday
network of interaction fun, and thus provide me with a never-ending source of
energy. Finally, this volume enabled me to collaborate with people whose
work I admire, and to this end, I thank all the volume contributors for being who they are.


Introduction and Keynote to A
Networked Self

Albert-László Barabási

Good morning. Today I’m going to talk about network science. My goal in the
light of the presentations we have today is to offer a rather different perspective:
that is, to argue that many of the things we see in the social environment
are rooted in some fundamental laws that not only social systems obey, but are
obeyed by a wide array of networks. Social systems are one of the most
powerful examples of networks because we understand and relate to them in
an everyday fashion. In a social network the nodes are the individuals and the
links correspond to relationships—who is talking to whom, who is communicating
with whom on a regular basis. What I would like to do today is to
examine how we think about such networks. Let’s assume that you’ve been
given the full set of relationships in a social network website such as Facebook.
How would you analyze the data of such density and richness?
If we think about these types of networks in mathematical terms, we have
to go back to mathematicians Pál Erdo˝s and Alfréd Rényi and the question they
asked about how to model a real network. As mathematicians, they thought of
networks in fundamentally simple terms: nodes and links. But the challenge
for these mathematicians was that they didn’t know how—in nature or society—
nodes decided to link together. So Erdo˝s and Rényi made the assumption
that links are assigned randomly, which means that any two nodes had a
certain probability of being connected, making the network a fundamentally random object.

Since 1960, mathematicians have invested a huge amount of work in understanding
these random networks. As an illustration, if we start with a probability
of p = 0, which means that the probability that any node is connected to
another node is zero, and add new nodes while increasing the probability of a
connection by adding links to the networks, clusters will start to emerge. If
we continue to add more links to the system, at a certain moment these clusters
will start joining each other. This is when the network actually emerges.

So there is this “magical” moment that mathematically takes us from lots of

disconnected clusters to the emergence of what mathematicians call a “giant

component.” When networks emerge through this process, it is very sudden.

So, we find ourselves with two questions. First, is this representation of how a

network emerges correct? And second, what does it mean?

Let’s first address the “What does it mean?” question. One of the premises

of a random network is that if you count how many links each node has, which

we call the “degree of distribution” of the network, you will find a Poisson distribution.

This means that if Facebook was a random network, you would find

that most individuals have approximately the same number of friends, and that

there are only very few individuals who have a very large number of friends or

have no friends whatsoever. In fact, when it comes to their circle of friends,

most individuals would be similar to each other. In a sense, the random

network describes a society that is fundamentally very democratic: everyone

has roughly the same number of friends, and it’s very difficult to find individuals

that are significantly richer or significantly poorer in the terms of their

social ties than the average person. So, despite the randomness by which the

links are placed, the randomness gets averaged out, and in the end we all

become very similar to each other.

Now, we need to question whether this is correct. Do we honestly believe

that real networks—society, the Internet, or other systems—are truly

random, decided by chance? No one would question that there is a large

degree of randomness in the way we make friends and in the way certain

things are connected. But is that all, or is there more to it? To answer this

question, about a decade ago we started to collect large data sets, large maps

of networks, with the idea that we needed to examine real networks to understand

how they actually worked. Our first choice was the World Wide Web, a

large network where nodes and documents were linked using URLs. It wasn’t

a philosophical decision, it was simply available data that we could actually

map out. We started in 1999 from the main page of University of Notre Dame

and followed the links. Then we followed the links on the pages we reached.

It was a terribly boring process, so we built a software to do this—these days,

it is called a search engine. But unlike Google, who runs similar search

engines, we didn’t care about the content of the pages. We only cared about

the links and what they were actually connected to. So at the end of the day,

this robot returned a map in which each node corresponds to a Web page and

the links tell you the connection to another page that can be made with a single click.

What was our expectation? Well, Web pages are created by individuals

who significantly differ from one another. Some people care about social

systems. Others care about the Red Sox or the White Sox, and still others care

about Picasso. And what people put on Web pages reflect these personal interests.

Given the huge differences between us, it’s reasonable to expect that a

very large network would have a certain degree of randomness. And we

expected that when we counted how many links each Web page had, the

network would follow Poisson distribution, as predicted by the random

network model. Surprisingly, however, our results showed something different.

We found a large number of very small nodes with only a few links each,

and a few very highly connected nodes. We found what we call a “power law

distribution.” That is, P(k) ~ k–γ where P(k) is the probability that a node has k

links and is called the “degree exponent.”

What is a power law distribution? A power law distribution appears on a

regular plot as a continuously and gradually decreasing curve. Whereas a

Poisson distribution has an exponentially decaying tail, one that drops off very

sharply, a power law distribution has a much slower decay rate resulting in a

long tail. This means that not only are there numerous small nodes, but that

these numerous small nodes coexist with a few very highly connected nodes, or hubs.

To illustrate, a random network would look similar to the highway system

of the United States, where the cities are the nodes and the links are the highways

connecting them. Obviously, it doesn’t make sense to build a hundred

highways going into a city, and each major city in the mainland U.S. is connected

by a highway. So if you were to draw a histogram of the number of

major highways that meet in major cities, you would find the average to be

around two or three. You wouldn’t find any city that would have a very large

number of highways going in or out. In comparison, a map of airline routes

shows many tiny airports and a few major hubs that have many flights going in

and out; these hubs hold the whole network together. The difference between

these two types of networks is the existence of these hubs. The hubs fundamentally

change the way the network looks and behaves. These differences

become more evident when we think about travel from the east coast to west

coast. If you go on the highway system, you need to travel through many

major cities. When you fly, you fly to Chicago and from Chicago you can reach

just about any other major airport in the U.S. The way you navigate an airline

network is fundamentally different from the way you navigate the highway

system, and it’s because of the hubs.

So we saw that the Web happens to be like the airline system. The hubs are

obvious—Google, Yahoo, and other websites everybody knows—and the

small nodes are our own personal Web pages. So the Web happens to be this

funny animal dominated by hubs, what we call a “scale-free

network.” When I

say “scale-free

network,” all I mean is that the network has a power law distribution;

for all practical purposes you can visualize a network as dominated by

a few hubs. So we asked, is the structure of the Web unique, or are there

other networks that have similar properties?

Take for example the map of the Internet. Despite the fact that in many

people’s minds the Internet and Web are used interchangeably, the Internet is

very different from the Web because it is a physical network. On the Web, it

doesn’t cost any more money to connect with somebody who is next door

than it does to connect to China. But with the Internet, placing a cable

between here and China is quite an expensive proposition.

On the Internet the nodes correspond to routers and the links correspond

to physical cables. Yet, if one inspects any map of the Internet, we see a couple

of major hubs that hold together many, many small nodes. These hubs are

huge routers. Actually, the biggest hub in the United States is in the Midwest,

in a well-guarded

underground facility. We’ll see why in a moment. Thus,

like the Web, the Internet is also a hub-dominated

structure. I want to empha

size that the Web and the Internet are very different animals. Yet, when you

look at their underlying structures, and particularly if you mathematically

analyze them, you will find that they are both scale-free networks.

Let’s take another example. I’m sure everybody here is familiar with the

Kevin Bacon game, where the goal is to connect an actor to Kevin Bacon.

Actors are connected if they appeared in a movie together. So Tom Cruise has

a Kevin Bacon number one because they appeared together in A Few Good Men.

Mike Myers never appeared with Kevin Bacon—but he appeared with Robert

Wagner in The Spy Who Shagged Me, and Robert Wagner appeared with Kevin

Bacon in Wild Things. So he’s two links away. Even historical figures like

Charlie Chaplin or Marilyn Monroe are connected by two to three links to

Bacon. There is a network behind Hollywood, and you can analyze the historical

data from all the movies ever made from 1890 to today to study its structure.

Once again, if you do that, you will find exactly the same power law

distribution as we saw earlier. Most actors have only a few links to other actors

but there are a few major hubs that hold the whole network together. You

may not know the names of the actors with few links because you walked out

of the movie theater before their name came up on the screen. On the other

hand there are the hubs, the actors you go to the movie theater to see. Their

names are on the ads and feature prominently on the posters.

Let’s move to the subject of this conference, online communities. Here,

the nodes are the members. And though we don’t know who they are, their

friends do, and these relationships with friends are the links. There are many

ways to look at these relationships. One early study from 2002 examined

email traffic in a university environment, and sure enough, a scale-free

network emerged there as well. Another studied a pre-cursor to Facebook, a

social networking site in Sweden, and exactly the same kind of distribution

arose there. No matter what measure they looked at, whether people just

poked each other, traded email, or had a relationship, the same picture

emerged: most people had only few links and a few had a large number.

But all the examples I have given you so far came from human-made

systems, which may suggest that the scale-free property is rooted in something

we do. We built the Internet, the Web, we do social networking, we do

email. So perhaps these hubs emerge as something intrinsic in human behavior.

Is it so?

Let’s talk about what’s inside us. One of the many components in humans

is genes, and the role of the genes is to generate proteins. Much of the dirty

work in our cells is done not by the genes, but by the proteins. And proteins

almost never work alone. They always interact with one another in what is

known as protein–protein interaction. For example, if you look in your blood

stream, oxygen is carried by hemoglobin. Hemoglobin essentially is a molecule

made of four proteins that attach together and carry oxygen. The proteins are

nodes in a protein–protein interaction network, which is crucial to how the

cell actually works. When it’s down, it brings on disease. There’s also a metabolic

network inside us, which takes the food that you eat and breaks it down

into the components that the cells can consume. It’s a network of chemical

reactions. So the point is that there are many networks in our cells. On the left-hand

side of this figure is the metabolic network of the simple yeast organism.

On the right-hand side is the protein–protein interaction network. 

In both cases, if you analyze them mathematically you will observe a scale-free

network; visually you can see the hubs very clearly.

When you think about it, this is truly fascinating because these networks

have emerged through a four-billion-year evolution process. 

Yet they converge to exactly the same structure that we observe for our social networks,

which raises a very fundamental question. How is it possible that cells and

social networks can converge with the same architecture?

One of the goals of this talk is to discuss the laws and phenomena that are

recurrent in different types of networks, summarizing them as organizing principles.

The first such organizing principle is the scale-free property which

emerges in a very large number of networks. For our purposes, it just simply

means that many small nodes are held together by a few major hubs. Yet,

there is a second organizing property that many of you may be aware of, often

called either the “six degrees” or the “small world” phenomenon. The idea

behind it is very straightforward: you pick two individuals and try to connect

them. For example, Sarah knows Ralph, Ralph knows Jason, Jason knows

Peter, so you have a three-handshake distance between Sarah and Peter. This

phenomenon was very accurately described in 1929 by the Hungarian writer

Frigyes Karinthy, in a short story that was published in English about two years

ago and translated by a professor at UIC, Professor Adam Makkai. The idea

entered the scientific literature in 1967 thanks to the work of Stanley Milgram,

who popularized the “six degrees of separation” phrase after following the path

of letters sent out from a particular town.

No matter what network you look at, the typical distances are short. And

by short we mean that the average separation between the nodes is not a function

of how many nodes the network has, but rather the logarithm of the

number of nodes, which is a relatively small number. This is not a property of

social networks only. We see it in the Web. We see it in the cell. We see it in

all different types of networks. The small world phenomenon is important

because it completely destroys the notion of space. Indeed, two people can be

very far away if you measure their physical distance. And yet, when you look

at the social distance between them, it is typically relatively short.

Now let’s come back to the central question that I raised earlier. I have

given several examples of networks that were documented to be scale-free.

How is it possible that such different systems—the Web, the Internet, the

cell, and social networks—develop a common architecture? What’s missing

from the random network model that doesn’t allow us to capture the features

of these networks? Why are hubs in all these networks?

To answer these questions, we must return to the random model, to Erdo˝s

and Rényi’s hypothesis, which contains several assumptions that you might not

have noticed. Their model depicts a society of individuals by placing six billion

dots on a screen and connecting them randomly. But their fundamental

assumption is that the number of nodes remains unchanged while you are

making the connections. And I would argue that this is not necessarily correct.

The networks we see have always gone through, and continue to go through,

an expansion process. That is, they are always adding new nodes, and this

growth is essential to the network.

Let’s inspect the Web. In 1991 there was only one Web page out there,

Tim Berners-Lee’s famous first page. And now we have more than a trillion.

So how do you go from one to more than a trillion nodes? The answer is one

node at a time, one Web page at a time, one document at a time, whether a

network expands slowly or fast, or does so node-by-node. So if we are to

model the Web, we can’t just simply put up a trillion nodes and connect

them. We need to reproduce the process by which the network emerged in

the first place. How would we do that? Well you assume that there is growth

in the system, by starting with a small network and adding new nodes, and

somehow connecting the new nodes to existing nodes.

The next question that comes up right away: how do we choose where to

connect the node? Erdo˝s and Rényi actually gave us the recipe. They said,

choose it randomly. But this is an assumption that is not borne out by our data.

It turns out that new nodes prefer to link to highly connected nodes. The Web

is the best example. There are a trillion pages out there. How many do you

know personally? A few hundred, maybe a thousand? We all know Google and

Yahoo, but we’re much less aware of the rest of the trillion which are not so

highly connected. So our knowledge is biased toward pages with more connections.

And when we connect, we tend to follow our knowledge. This is

what we call “preferential attachment” and simply means that we can connect

to any node, but we’re more likely to connect to a node with a higher degree

than to one with a smaller degree. It’s probabilistic: the likelihood of me connecting

to a certain Web page is proportional to how many links that page

already has. This is often called the “Matthew Effect” from Merton’s famous

paper, and is also sometimes called “cumulative advantage.” The bottom line is

that there is a bias toward more connected nodes. If one node has many more

links than another, new nodes are much more likely to connect to it. So, big

nodes will grow faster than less connected nodes.

One of the most beautiful discoveries of random network theory is that if

we keep adding links randomly, at a certain moment a large network will suddenly

emerge. But the model discussed above suggests a completely different

phenomenon: the network exists from the beginning, and we just expand it.

There is no magic moment of the emergence of the network. In evolving

network theory, we look at the evolution of the system rather than the sudden

emergence of the system. So if we take this model and grow many nodes, you

will find that the emerging network will be scale-free

and the hubs will naturally

emerge. This is the third organizing principle: hubs emerge via growth

and preferential attachment.

Now let’s be realistic. There are lots of other things going on in a complex

networked system in addition to those I have just described. One thing we

learned mathematically is that as long as the network is growing, and as long as

there is some process that generates preferential attachment, a network is scale-free.

Thus, one of the reasons there are so many different networks that are scale-free

is because the criteria for their emergence is so minimal.

The next question that naturally comes up concerns one of this model’s

predictions: the earliest nodes in the network become the biggest hubs. And

the later the arrival, the less chance a node has to become big. There is way of

mathematically expressing this occurrence: each node increases its degree as

the square root of time. This means that the longer you are in the system, the

more connected you are. So, can any of us become hubs if we are late-comers?

Well, there are obvious examples of this happening. Google was a relative

latecomer to the WWW and yet it’s the biggest hub today. 

So, how can you be a late-comer

and become very highly connected? Is there a mechanism for this?

One way to describe the Google phenomenon is with the concept of fitness.

What is fitness? Fitness is the node’s ability to attract links. It’s not the likelihood

of finding a Web page, but rather once you’ve found a Web page, it’s

the probability that you will connect to it. It’s not the chance of running into a

person. But once you’ve met the person, will you want to see him or her

again? Thus, fitness is the ability to attract links after these random encounters.

To model the impact of fitness, we assign a parameter for each node which

represents its ability to compete for links. You can build it into preferential

attachment, because now the likelihood that you will connect to a certain node

is the product of the fitness and the number of links. The number of links is

there because it tells us how easy it is to find the node. If a node is very highly

connected, it is easy to bump into it. But the fitness tells me the likelihood that

I will actually link to it, once I find it.

If you solve this fitness-driven

model analytically, you will find that each

node will increase its links following a power law, but the exponent by which

the node grows is unique to the node. What does this mean? It means that

there’s a possibility for a node to come in late with a higher fitness and grow

faster than the earlier-arriving

nodes. Now, if the fitness of the new node is

only marginally higher than the other nodes, it will take a long time to catch

up. But if it’s significantly higher, then the node will actually grow larger than

any of the others. One of the reasons it’s so hard to beat Google today—that

is, to grow as large as Google is as a late-comer—

is that there has to be a significantly

higher fitness to overcome the time lag.

Fitness also makes a somewhat disturbing prediction, allowing for the possibility

of a “winner takes all” situation. In the language of physics, this is what

we call a “Bose–Einstein condensation,” and simply means that a node with

significantly higher fitness will grab all the links. As the network grows, this

node will completely dominate the system, much more so than a hub in a scale-free

network. Let me explain the difference between a scale-free

network and a “winner takes all” network. In a scale-free network, as the

network expands, the market share of the biggest hub will decrease in time.

That is, even though the biggest hub will get larger and larger, the fraction of

the total links in the full network that connect to it will slowly decay. In a case

where you have a “winner takes all” situation, the market share of the biggest

hub will remain constant. An example is the Windows operating system,

which has an 85% market share in operating systems. That’s a winner takes all

situation because its share has stayed relatively constant over that of Apple and

Linux. So, to summarize, competition in networks is driven by fitness; the

fittest nodes are the ones who will turn slowly into hubs. So it’s very important

to think about where fitness comes from. And, obviously, if you want to

compete, you need to think about how to increase your fitness.

The next questions that come up are, “So what—should we even care?” and

“Do these hubs have any consequences that are important?” It turns out that

there are many consequences. One is illustrated by the concept of robustness,

which means that complex systems maintain their basic functions even under

errors and failures. For example, in my cells there are many errors. Yet I can

carry on speaking, despite the fact that something in my cells has gone wrong.

Another example is the Internet, where at any time hundreds of routers are

not working, yet the Internet still functions. So how do we think about the

concept of robustness in the network context? Well, we can model a network

and see what happens when a couple of nodes break down or disappear from

the system. For a very large random network, we can delete randomly chosen

nodes to see how the network will support that process. There is a very

precise mathematical prediction about random networks that says that if you

start removing nodes, you will reach a critical point at which the network will

fall apart. That is, every random network and every regular network, like a

square lattice or triangular lattice, will have this critical point. By removing

more nodes than this critical threshold, the network will break apart; it is unavoidable.

What happens in a scale-free

network? It turns out that we can remove a

significant fraction of the nodes without breaking it apart. What’s going on

here? By randomly removing the nodes, in a scale-free network we are typically

removing small nodes, because there are so many of them. The probability

of removing a hub is very low, as there are only a few hubs. Yet, removing a

small node just means the network becomes slightly smaller. It shrinks, but

doesn’t fall apart. In fact, we can remove 98% of the nodes in a large scale-free

network, and the remaining 2% will stay together and continue to communicate.

There is a built-in robustness to this network because of the

hubs—but there’s also a price to pay. What if we remove nodes not randomly,

but in an attack mode? That is, we remove the biggest hub, the next biggest

hub, and so on. In this case the network breaks into pieces very quickly. Scale-free

networks have this amazing property of robustness to random failures,

but they are also very fragile. If we know what the system looks like, we can

destroy it very easily. This is why the Midwest router is so heavily protected.

And so our fourth organizing property of scale-free

networks becomes robustness

against failure with vulnerability to attack.

What about communities within networks? We know that most networks

are full of communities or groups of nodes that tend to connect more to each

other than we would expect randomly. We can visualize these as groups of

people in the same class or department, who all know each other. But the

existence of these communities produces a tension with the scale-free

property of networks. The scale-free

property suggests that we have a few hubs

that hold the whole network together, and the communities suggest that there

are relatively isolated groups of nodes that work independently.

So can we bring the two together? It turns out we can, but it implies

another constraint on the network, what we call a “hierarchical network.” To

illustrate a hierarchical network, let’s begin with a small community and

create four copies of it, connecting each with the previous one. Repeat this

again and again. It turns out that this network has a hierarchical structure that

can be mathematically measured. It has signatures that are present in many

networks—social networks, the Web, and the Internet. The smaller communities

are highly interconnected, while the larger communities are less

dense. As communities get larger, they become less dense and they connect to

each other in a hierarchical fashion.

Networks exist for a reason. They spread ideas; they spread knowledge;

they spread influence. What happens if you give a piece of information to an

individual, who passes it on to friends, who then pass it on to their friends,

and so on? What does this information network look like? Let me show you an

example. This figure shows a small neighborhood in a fully anonymized phone

Table of Contents

Acknowledgments viii

Introduction and Keynote to A Networked Self

Albert-László Barabási

Part I

Context: Communication Theory and Social

Network Sites 15

1 Interaction of Interpersonal, Peer, and Media Influence

Sources Online: A Research Agenda for Technology

Convergence 17

Joseph B. Walther, Caleb T. Carr, Scott Seung W. Choi,

David C. De Andrea, Jinsuk Kim, Stephanie Tom Tong,

AND Brandon Van Der Heide

2 Social Network Sites as Networked Publics: Affordances,

Dynamics, and Implications 39

danah boyd

3 Social Networking: Addictive, Compulsive, Problematic,

or Just Another Media Habit? 59

Robert La Rose, Junghyun Kim, and Wei Peng

4 Social Network Exploitation 82

Mark Andrejevic

Part II

Social Textures: Emerging Patterns of Sociability

on Social Network Sites 103

5 Social Network Sites as Virtual Communities 105

Malcolm R. Parks

6 With a Little Help from My Friends: How Social

Network Sites Affect Social Capital Processes 124

Nicole B. Ellison, Cliff Lampe, Charles Steinfield,

and Jessica Vitak

7 From Dabblers to Omnivores: A Typology of Social

Network Site Usage 146

Eszter Hargittai and Yu-Li Patrick Hsieh

8 Exploring the Use of Social Network Sites in the

Workplace 169

Mary Beth Watson - Manheim

Part III

Convergent Practices: Intuitive Appropriations

of Social Network Site Affordances 183

9 United We Stand? Online Social Network Sites and Civic

Engagement 185

Thomas J. Johnson, Weiwu Zhang, Shannon L. Bichard,

and Trent Seltzer

10 Between Barack and a Net Place: Motivations for Using

Social Network Sites and Blogs for Political Information 208

Barbara K. Kaye

11 Working the Twittersphere: Microblogging as

Professional Identity Construction 232

Dawnr .Gilpin

12 Look At Us: Collective Narcissism in College Student

Facebook Photo Galleries 251

Andrew L. Mendelson and Zizi Papacharissi

13 Copyright, Fair Use, and Social Networks 274

Patricia Aufderheide

14 Artificial Agents Entering Social Networks 291

Nikolaos Mavridis

Conclusion: A Networked Self 304

Zizi Papacharissi

About the Editor 319

List of Contributors 320

Index 325

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