Zizi Papacharissi-A Networked Self_ Identity, Community, and Culture on Social Network Sites-Routledge英文书籍资料.pdf

A Networked SelfA 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 SelfIdentity, Community, and Culture on Social Network SitesEdited by Zizi PapacharissiFirst published 2011 by Routledge 270 Madison Avenue, New York, NY 10016Simultaneously published in the UK by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RNRoutledge is an imprint of the Taylor these hubs hold the whole network together. The difference between these two types of networks is the existence of these hubs. The hubs funda- mentally 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 its because of the hubs.So we saw that the Web happens to be like the airline system. The hubs are obviousGoogle, Yahoo, and other websites everybody knowsand 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 distri- bution; 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 peoples 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 doesnt 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. Well see why in a moment. Thus, like the Web, the Internet is also a hub- dominated structure. I want to empha-Introduction and Keynote to A Networked Self 5size 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.Lets take another example. Im 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 Baconbut he appeared with Robert Wagner in The Spy Who Shagged Me, and Robert Wagner appeared with Kevin Bacon in Wild Things. So hes 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 histor- ical data from all the movies ever made from 1890 to today to study its struc- ture. 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.Lets move to the subject of this conference, online communities. Here, the nodes are the members. And though we dont 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 behav- ior. Is it so?Lets talk about whats 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 proteinprotein interaction. For example, if you look in your blood stream, oxygen is carried by hemoglobin. Hemoglobin essentially is a molecule 6 Introduction and Keynote to A Networked Selfmade of four proteins that attach together and carry oxygen. The proteins are nodes in a proteinprotein interaction network, which is crucial to how the cell actually works. When its down, it brings on disease. Theres also a meta- bolic network inside us, which takes the food that you eat and breaks it down into the components that the cells can consume. Its 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 organ- ism. On the right- hand side is the proteinprotein 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.Figure I.2 Protein interaction network of yeast, an organism often studied in biologi- cal labs. Each node corresponds to a protein and two proteins are linked together if there is experimental evidence that they interact with each other in the cell. The color of the nodes denote their essentiality: dark grey proteins are those without which the organism cannot survive, while light grey are those that the organism can live without. Note the uneven link distribution: most proteins link to one or a few nodes only, while a few proteins act as hubs, having links to dozens of other proteins.Introduction and Keynote to A Networked Self 7When you think about it, this is truly fascinating because these networks have emerged through a four- billion-year evolution process. Yet they con- verge 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 prin- ciples. 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 func- tion 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 lets 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 systemsthe Web, the Internet, the cell, and social networksdevelop a common architecture? Whats missing from the random network model that doesnt 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 Rényis 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 8 Introduction and Keynote to A Networked Selfmaking 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.Lets inspect the Web. In 1991 there was only one Web page out there, Tim Berners- Lees 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 cant 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 were much less aware of the rest of the trillion which are not so highly connected. So our knowledge is biased toward pages with more con- nections. 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 were more likely to connect to a node with a higher degree Figure I.3 Birth of a scale-free network. The scale-free topology is a natural con- sequence of the ever-expanding nature of real networks. Starting from two connected nodes (top left), in each panel a new node, which is shown as an open dot, is added to the network. When deciding where to link, new nodes prefer to attach to the more connected nodes. Thanks to growth and preferential attachment, a few highly connected hubs emerge.Introduction and Keynote to A Networked Self 9than to one with a smaller degree. Its probabilistic: the likelihood of me con- necting to a certain Web page is proportional to how many links that page already has. This is often called the “Matthew Effect” from Mertons 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 sud- denly 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 natu- rally emerge. This is the third organizing principle: hubs emerge via growth and preferential attachment.Now lets 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 models predictions: the earliest nodes in the network become the biggest hubs. And the later the arrival, the