Archive for the ‘networks’ Category
university of chicago visit – everything you wanted to know about tweets and votes, but were afraid to ask
I will be a guest of the computational social science workshop at the University of Chicago this coming Friday. I will present a very detailed talk on the more tweets/more votes phenomena called “Everything You Wanted to Know About the Tweets-Votes Correlation, but Were Afraid to Ask.” If you want to chat or hang out, please email me.
Refreshments will be served.
Last Saturday, Andrew Gelman responded to a post about a discussion in my social network analysis course. In that post, my student asked about different strengths of a network effect reported in a paper. Gelman (and Cosima Shalizi) both noted that the paper does not show a statistically significant difference. I quote the concluding paragraphs of Andrew’s commentary:
I’m doing this all not to rag on Rojas, who, after all, did nothing more than repeat an interesting conversation he had with a curious student. This is just a good opportunity to bring up an issue that occurs a lot in social science: lots of theorizing to explain natural fluctuations that occur in a random sample. (For some infamous examples, see here and here.) The point here is not that some anonymous student made a mistake but rather that this is a mistake that gets made by researchers, journalists, and the general public all the time.
I have no problem with speculation and theory. Just remember that if, as is here, the data are equivocal, that it would be just as valuable to give explanations that go in the opposite direction. The data here are completely consistent with the alternative hypothesis that people follow their spouses more than their friends when it comes to obesity.
Fair enough. Let me add a pedagogical perspective. When I teach network science to undergrads, I generally have a few goals. First, I want to show them how to convert social tie data into a matrix that can be analyzed. Second, I want students to learn how network concepts might operationalize social science concepts (e.g., how group cohesion might be described as high density). Third, I want to spark their imagination a little and see how network analysis can be used to describe or analyze a wide range of phenomena and thus encourage students to generate explanations. Given that students have very, very modest math skills and real problems generating hypotheses, getting down into the weeds with the papers is often last.
So when I teach the week on networks and health, my discussion questions are like this: “Why do you think health might be transmitted from one person to another? How would that work?” I also try to get into basic research design: “How do you measure health? Do you know what BMI is?” So the C&F paper has many up sides. The downside is that the paper has an interesting hypotheses and you can easily get distracted from the methodological controversy the paper has generated, or even some very sensible observations on confidence intervals. The bottom line is that when you have to teach everything (theory, methods, research design and topic), you don’t quite get everything. But still, if a student, who self-admitedly knows little math or stats, can get to a point about asking about mechanisms, then that’s a teaching victory.
After reading the Fowler/Christakis paper on networks and obesity, a student asked why it was that friends had a stronger influence on spouses. In other words, if we believe the F&C paper, they report that your friends (57%) are more likely to transmit obesity than your spouse (37%) (see page 370).
This might be interpreted in two ways. First, it might be seen as a counter argument. This might really indicate that homophily is at work. We probably select spouses for some traits that are not self-similar. While we choose friends mainly on self-similarity of leisure and consumption (e.g, diet and exercise). Second, there might be an explanation based on transmission. We choose friends because we want them to influence us, while spouses are (supposed?) to accept us.
“There’s a literature on everything.” – Tyler Cowen
Yup, it turns out that not only is there is a network analysis literature on mean girls, but it has been published in the ASR. I quote from an article by Bob Faris and Diane Felmlee called “Status Struggles: Network Centrality and Gender Segregation in Same- and Cross-Gender Aggression:”
Literature on aggression often suggests that individual deficiencies, such as social incompetence, psychological difficulties, or troublesome home environments, are responsible for aggressive behavior. In this article, by contrast, we examine aggression from a social network perspective, arguing that social network centrality, our primary measure of peer status, increases the capacity for aggression and that competition to gain or maintain status motivates its use. We test these arguments using a unique longitudinal dataset that enables separate consideration of same- and cross-gender aggression. We find that aggression is generally not a maladjusted reaction typical of the socially marginal; instead, aggression is intrinsic to status and escalates with increases in peer status until the pinnacle of the social hierarchy is attained. Over time, individuals at the very bottom and those at the very top of a hierarchy become the least aggressive youth. We also find that aggression is influenced not so much by individual gender differences as by relationships with the other gender and patterns of gender segregation at school. When cross-gender interactions are plentiful, aggression is diminished. Yet these factors are also jointly implicated in peer status: in schools where cross-gender interactions are rare, cross-gender friendships create status distinctions that magnify the consequences of network centrality.
I just wrapped up my undergrad course in networks for seniors. Near the end, in the week on networks and crime, we discussed Papachristos’ work on homicide in Chicago. If you haven’t read it, he has a very rich data set on gangs and traces the back and forth of gang revenge homicides. Great stuff. So I asked my students: “You are the police and now you have read this research, what did you learn?”
Student 1: You should target the most central gangs. They seem to generate a lot of violence.
Me: Good, what else?
Student 1: Since a lot seems to focus on revenge, maybe police should focus on friends of homicide victims. Maybe counsel them so they won’t get revenge and keep the cycle going.
Student 2: That would never work.
Student 2: The cops gets no credit for counseling. Only for arrests.
Bingo. Great insight. In other words, we have a lot of good data on homicides and we know that a lot of it has to do with gang/revenge cycles. And that implies a solution – go after survivors and do what you can to keep them from acting out. But it is very hard to see how anyone could ever be rewarded in the system where people get promoted for arrests rather than crime prevention. It’s sad that you need have someone murdered first before you can be praised for being a good cop.
More Tweets, More Votes news:
- I thank Alex Hanna for mentioning this work in a new Foreign Policy piece that discusses how social media can be used to monitor elections in nations where polling is rare, a possibility that I mentioned in my Washington Post article on MTMV. Alex and co-author Kevin Harris use social media data to track Iranian public opinion, because quality polling is not common there. A must read for people who want to see how social media can be used to measure and evaluate democratic processes.
- The peer reviewed version of MTMV is now out in PLoS One. The paper presents the tweet share/vote share correlation for the 2010 and 2012 House elections and discusses possible mechanisms.
- The working paper version of MTMV at Social Science Research Network has had over 1,200 downloads in its short life, pushing it into the top 10 most downloaded papers on models of elections and political processes at SSRN. Congratulations to my co-authors Joe DiGrazia, Karissa McKelvey, and Johan Bollen. Outstanding work.
Insider tip: New results be presented at the computational social science workshop at the University of Chicago in January 2014. Details forthcoming.
A lot of sociologists buy into the theory of “sponsored mobility,” which means that elites pick who gets the mobility. So I think there should be a lot of sympathy for recent research showing that mentorship (communicating with more advanced people) does not have an effect on career advancement but sponsors (people who pick you, push you, and get benefit from it) do have an effect. Robin Hanson reviews a book by economist Sylvia Ann Hewett that makes this claim:
In a new book, economist Sylvia Ann Hewlett uses data to show that mentorship, in its classic wise-elder-advises-younger-employee form, doesn’t produce statistically significant career gains. What does however, her research found, is something she has termed “sponsorship”—a type of strategic workplace partnering between those with potential and those with power. … -
And there is an important implication for the study of gender and inequality:
Women are only half as likely as men to have a sponsor—a senior champion at work who will basically take a bet on them, tap them on the shoulder, and really give them a shot at leadership. Women have always had mentors, friendly figures who give lots of advice. They’re great. They’re good for your self-esteem; they’re good for your personal development. But no one’s ever been able to show that they do anything to help you actually move up. …
We find that women in particular often choose the wrong people. … They seek out a senior person they’re very comfortable with. … For a sponsor, you should go after the person with power, because you need someone who has a voice at those decision-making tables. You need to respect that person, you need to believe that person is a fabulous leader and going places, but you don’t need to like them. You don’t need to want to emulate them.
If true, this forces me to modify my views. I have always believed that sponsored mobility is important in academia, but I believe that mentorship matters as well. If Hewett is right, my belief is misplaced. It’s really about sponsored mobility. So, if you care about women or minorities advancing in some career track (like academia), then forget the nice lunches. Administrators should double down on matching people with power players. A bit rude, but it might be one concrete way to chip away at inequality in the leadership of the academy.
At last week’s PLEAD conference on social media and political processes, Alex Hanna tweeted a summary of a talk by Mark Huberty of UC Berkeley political science, which raised some questions about using social media data to forecast electoral results. Alex suggested that we could have a good discussion about Mark’s talk. In these comments, I rely on Alex’s summary. If I mis-characterized a point, please email me or correct me in the comments.
1. Huberty noted, correctly, that incumbency highly correlates with electoral wins. The implication is that social media data is not valuable, or important, or accurate, because incumbency accounts for a lot of the variance in electoral outcomes.
Well, it depends on what your goals are. If you are making a claim that “A causes B”, then finding out that C account for much of the variance is extremely important. It shows that A isn’t causing B. However, if your claim is that “A is a decent measurement of B,” then finding out that C is a strong correlate of B is simply irrelevant. The claim isn’t about what is some fundamental cause of B, just what tracks with B.
Different claim, different standard of proof. That’s we care about polls. Incumbency predicts elections better than polls, but as long as we don’t claim that polls cause election outcomes, we remain satisfied with the well documented correlation between voter surveys and final votes.
Also, incumbency is not a reasonable variable to benchmark against because incumbency is simply a word for “the person who won last time in the same election with a very similar group of voters.” As good social scientists know, a lot of human behavior is seriously auto-correlated. What I ate yesterday is the best predictor of what I’ll eat tomorrow. Politics is no different.
Thus, in a lot of social science, we aren’t interested in these sorts of time series because we know that answer already. X_t is almost certainly strongly correlated with X_t-1. The interesting question is why the time series is X_1, X2,… and not Y_1, Y_2, … Similarly, we might interested in “extracting a signal” from some new source of data to help us measure X_i or build a causal explanation that doesn’t fall back on trivial auto-correlated time series explanations. In other words, “The guy is an incumbent because there are a lot Black voters” is a much more meaningful statement than “The guy won this time because he won last time.”
That is ultimately why I remain interested in social media and electoral outcomes. Social media is a record of what people think that is different than polls and traditional print or broadcast media. It deserves a serious examination as a signal. And given the work by Huberty himself, Tusmajan, Juengher, Beuchamp, the Indiana group, and others, the “social media as measurement of political sentiment” hypothesis is important and, as far as I can tell, supported to varying degrees by the Twitter data. Incumbency is a non-issue as long as researchers and political professionals avoid claims of causation.
2. Alex also indicated that Mark Huberty was concerned about how social media data is created. Here, I also agree. Transparency is important. All data is imperfect – people lie on polls, surveys has selection biases, etc. There is a discussion about the properties of the samples that Twitter produces for researchers that might lead one to think that there might be an issue. The more we know about the way social media samples are generated, the better.
Still, the issue is *how much* of a problem this is. On this point, I urge Mr. Huberty to be bluntly empirical.The blunt empiricist, I would argue, would just put it to the test. The empiricist would look for natural experiments in the data (transparent data vs. others) or well chosen comparisons to see how much it affects the social media-vote correlation. Rather than point to possible problems, research would actually identify them. It might not matter, or it might be a big deal. Let’s figure it out!
In my undergraduate social network class, I tried to explain how social network analysis could be used to identify a certain “type” of person. I often use high schools as an example. One could ask students to identify friends and then use that data to map groups, cliques, and the like. At one point in the discussion, I then said, “for example, we could use network data to discover the most popular people, the MEAN GIRLS.” I then asked, “how would we discover mean girls?”
In our discussion, I think we settled on the following:
- Mean girls would have high centrality scores.
- With asymmetrical friendship network data, mean girls would not reciprocate.
- If people rated the content of the network tie, mean girls would receive a lot positives but send out negatives.
- Mean girls would cluster, or have structurally equivalent roles.
A student asked, “Fabio, were you a mean girl in high school?”
I said, “probably not, I was very shy and I rarely taunted kids or got in fights. In some ways, though, I am a mean nerd.”
The student responded, “Fabio, you are definitely a mean nerd. I read what you wrote about the critical realists.”
My dear friend and collaborator Michael T. Heaney has some new work that will be of interest to many readers. In the journal Social Networks, he has an article called Multiplex networks and interest group influence reputation: An exponential random graph model:
Interest groups struggle to build reputations as influential actors in the policy process and to discern the influence exercised by others. This study conceptualizes influence reputation as a relational variable that varies locally throughout a network. Drawing upon interviews with 168 interest group representatives in the United States health policy domain, this research examines the effects of multiplex networks of communication, coalitions, and issues on influence reputation. Using an exponential random graph model (ERGM), the analysis demonstrates that multiple roles of confidant, collaborator, and issue advocate affect how group representatives understand the influence of those with whom they are tied, after accounting for homophily among interest groups.
In the journal Interest Groups and Advocacy, he has a forthcoming article: Coalition Portfolios and Interest Group Influence Over the Policy Process, with Goeff Lorenz.
My colleague at Indiana University, Johan Bollen has patented an algorithm that allows him to link Twitter traffic to stock price fluctuations. Click on the link for the TV news item. A clip from the report:
An IU professor and researcher just received a patent for software that crunches hundreds of millions of tweets, to predict where the stock market is headed…
Think of this way: The thoughts of two or three million people probably don’t add up to much, but if you multiply that by tens or hundreds of millions of people, then you may have something.
“We find that when people get more anxious, then there is a great likelihood of the market dropping 3-4 days later and vice versa,” Bollen said.
Definitely check it out.
Recent research has shown a change in Facebook use. While users tend to retain accounts, people are now reducing their use of the website. The reasons? From a recent NY Times survey of Facebook users:
The main reasons for their social media sabbaticals were not having enough time to dedicate to pruning their profiles, an overall decrease in their interest in the site, and the general sentiment that Facebook was a major waste of time.
This may indicate that we’ve hit “peak Facebook,” in terms of the site’s popularity level. It’s now a standard tool for networking, but the novelty has worn off. People don’t feel the obligation to use it. Now, the main users will be people who really enjoy networking – young people, businesses/orgs and extroverted people. Still, a huge market, but far short of the all encompassing vision of some. Probably the time to dig deep into that “platform” strategy we were talking about.
Here’s how I view the history of social network analysis:
- Pre-history – Simmel (1900s) to Moreno (1930s): People start thinking about the “geometry” of social relationships.
- Network science 1.0 – Harary, Heider, Freeman, etc. (1950s – 1970s): People learn to convert relational data into matrix algebra.
- The holistic turn (1970s – 1980s): People start inventing measures of network structure (Bonacich, White).
- Statistical theory of networks (1970s-2000s): The creation of P* models, and later dynamic network models, to account for non-independence.
- Socio-physics networks (2000s): Watts, Barbasi, and others from physics work on large scale properties of networks (e.g., power laws or small worlds).
So, by my account, the last major development in network analysis was about 10 years ago. Now, this isn’t to say that there isn’t excellent work, but it is normal science. Pick up a copy of Social Networks, or Network Science. You’ll see great articles, but they are usually investigating specific networks, or figuring out the details of some specific. Am I missing the next generation of network analysis? One possibility is that there will be new ideas coming from people doing experiments on networks for estimate causal effects. Other areas?
A few recent articles from the journal Social Networks:
- Parigi and Sartori discuss party networks and cleavages in Italy.
- Networks and soccer team wins by Grund.
- Crossley, Edwards, Harries, and Stevenson discuss the trade off between efficacy and secrecy in movement networks.
- Amicus curiae (“friends of the court” briefing) networks by Box-Steffensmeir and Christensen
My collaborator, Michael Heaney, has a nice article in the new American Behavioral Scientist where he measures polarization in party networks:
Previous research has documented that the institutional behaviors (e.g., lobbying, campaign contributions) of political organizations reflect the polarization of these organizations along party lines. However, little is known about how these groups are connected at the level of individual party activists. Using data from a survey of 738 delegates at the 2008 Democratic and Republican national conventions, we use network regression analysis to demonstrate that co-membership networks of national party convention delegates are highly polarized by party, even after controlling for homophily due to ideology, sex/gender, race/ethnicity, age, educational attainment, income, and religious participation. Among delegates belonging to the same organization, only 1.78% of these co-memberships between delegates crossed party lines, and only 2.74% of the ties between organizations sharing common delegates were bipartisan in nature. We argue that segregation of organizational ties on the basis of party adds to the difficulty of finding common political ground between the parties.
Good for those interested in the growing literature on networks in political science.
The most recent Nature features an article by a team of political scientists and network scholars who did an experiment using Facebook to show that strong ties influenced voting behavior in the last election. You may say, so what? We’ve known for a long time that social influence operates through strong ties in interpersonal networks. That’s not a new insight. But I think the study is innovative for a couple of reasons. The first is that the impact of of using direct messaging through Facebook was substantively significant – that is, just messaging people reminders to go out and vote increased the likelihood that the person would vote – but that the larger effect was transmitted indirectly via social contagion. Consider the setup of the experiment.
A key empirical question in social network analysis is whether Americans have more or less friends over time. Famously, Robert Putnam argued that indeed, we were “bowling alone.” In contrast, critics contend that these are misinterpreted results. Some types of networks disappear, while other appear.
On the social network listserv, Claude Fischer provides the latest round in the debate. Fischer uses 2010 GSS data to claim that the decline in strong personal relationships reported by McPhereson et al. (2006 in the ASR) is due to survey question construction. I’ll quote Fischer’s entire announcement: Read the rest of this entry »
A focus of network research since, say 1999 or so, has been to identify “laws” that generate large networks with certain properties.* For example, the small world network is built by rewiring a grid. Various processes generate power-law networks (i.e., the node distribution is described by a power law).
I can see two justifications for this type of research. The first is diffusion theory. The speed at which something diffuses in a network is definitely governed by the structure. The second is a sort of physical science justification, where you think of a network as a “system” and you show that some micro-process (e.g., preferential attachment) creates that network.
Is there any other behavioral implication of studying power laws/small worlds or other specific large scale properties? In other words, why should I care about scale free or small world networks aside from diffusion theory?
* Let’s leave aside recent criticism of power-law centric research for the sake of the post.
Duncan Watts, the social science researcher who has been at Yahoo since 2007, has left the company.
Yahoo confirmed the departure. Watts has reportedly joined Microsoft’s research organization, but the software company declined to comment.
Comments? I’m excited to see what he does at his new job.
Eric Klinenberg is a sociologist who also happens to be a very good writer. Who needs a Malcolm Gladwell to popularize sociology when we already have good writers, like Klinenberg, in the discipline? His book Heat Wave: A Social Autopsy of Disaster in Chicago is an example of his ability to present empirical sociology in an engaging and lucid form.
Eric’s latest book, Going Solo: The Extraordinary Rise and Surprising Appeal of Living Alone, expands on a theme of Heat Wave: that living alone is a growing trend, especially in urban areas, that has changed the nature of community and relationships. In his former book Eric showed that the people most susceptible to the negative consequences of a major environmental disaster, like a heat wave, were those who lived alone and lacked a social safety net to assist them during the crisis. Although in Heat Wave he focused on the deleterious effects of “living and dying alone,” this book takes a broader perspective by first trying to understand why more people are making this life choice and then by examining its consequences on life quality.
One of the interesting insights of Going Solo is that living alone has become easier for people to do because there are so many ways in which people can create and flourish abundant social lives outside the home. Facebook, email, texting, and other social media provide numerous points of contact that shorten the social distance between friends and family. Someone who lived alone 30 years ago might have felt isolated because it was much more costly and difficult to maintain close contact with friends, but now personal communication with friends and family has become so easy to do that it can almost be overwhelming.
One woman we interviewed, an attorney in her early thirties who works in politics, tells me: ‘Of my nine-hour day, I’m spending seven hours responding to emails’ – mostly job related, but many from friends and family too. ‘I also have, like, three hundred fifty people in my cell phone,’ she explains. It buzzes often, she checks it constantly, and she always tries to respond quickly, even if she’s out with friends and the call or message is from work.
This behavior is not unusual. Although we often associate living alone with social isolation, for most adults the reverse is true. In many cases, those who live alone are socially overextended, and hyperactive use of digital media keeps them even busier. The young urban professionals we interviewed reported that they struggle more with avoiding the distraction of always available social activity, from evenings with friends to online chatter, than with being disconnected. ‘Singles in the U.S.: The New Nuclear Family’ confirms this. The large-scale study by the market research firm Packaged Facts reports that those who live alone are more likely than others to say that the Internet has changed the way they spend their free time, more likely to be online late at night, and more likely to say that using the Net has cut into their sleep. Not that they are homebodies. According to a Pew Foundation study of social isolation and technology, heavy users of the Internet and social media are actually more likely than others to have large and diverse social networks, visit public places where strangers may interact, and participate in volunteer organizations (pg. 64).
If people used to seek domestic life in order to avoid social isolation, social technology seems to have weakened some of that need. People, especially those who can afford to stay connected and have a busy social life, may find pairing up and having kids less appealing than ever.
This book is full of fascinating facts and anecdotes about why and how people manage to live alone. This would be a great book for undergraduate courses in urban/community sociology, social networks, social problems, or even an introductory course in sociology.
It’s been a while since we’ve knocked heads with our evil twin blog. I can’t let this one pass. Peter Klein misrepresents the main point of this Jonah Lehrer New Yorker article, which dissects the myth that brainstorming leads to creativity and greater problem solving. Citing a quote by former orgtheory guest blogger Keith Sawyer – “Decades of research have consistently shown that brainstorming groups think of far fewer ideas than the same number of people who work alone and later pool their ideas” – Peter implies that groups would be more creative if they’d just let individuals work on their own. This fits nicely with a pure reductionist perspective but it’s not at all what the article is really trying to say.
This is the conclusion that Peter should have drawn from the essay: “[L]ike it or not, human creativity has increasingly become a group process.” Lehrer goes on to cite research by my colleagues at Northwestern, Ben Jones and Brian Uzzi, which shows that both scientists and Broadway teams are more successful and creative when bringing together teams made up of diverse individuals. From an article in Science by Wuchty, Jones, and Uzzi:
By analyzing 19.9 million peer-reviewed academic papers and 2.1 million patents from the past fifty years, [Jones] has shown that levels of teamwork have increased in more than ninety-five per cent of scientific subfields; the size of the average team has increased by about twenty per cent each decade. The most frequently cited studies in a field used to be the product of a lone genius, like Einstein or Darwin. Today, regardless of whether researchers are studying particle physics or human genetics, science papers by multiple authors receive more than twice as many citations as those by individuals. This trend was even more apparent when it came to so-called “home-run papers”—publications with at least a hundred citations. These were more than six times as likely to come from a team of scientists.
And summarizing Uzzi’s and Spiro’s AJS paper on Broadway shows:
Uzzi devised a way to quantify the density of these connections, a figure he called Q. If musicals were being developed by teams of artists that had worked together several times before—a common practice, because Broadway producers see “incumbent teams” as less risky—those musicals would have an extremely high Q. A musical created by a team of strangers would have a low Q…..When the Q was low—less than 1.7 on Uzzi’s five-point scale—the musicals were likely to fail. Because the artists didn’t know one another, they struggled to work together and exchange ideas. “This wasn’t so surprising,” Uzzi says. “It takes time to develop a successful collaboration.” But, when the Q was too high (above 3.2), the work also suffered. The artists all thought in similar ways, which crushed innovation. According to Uzzi, this is what happened on Broadway during the nineteen-twenties, which he made the focus of a separate study. The decade is remembered for its glittering array of talent—Cole Porter, Richard Rodgers, Lorenz Hart, Oscar Hammerstein II, and so on—but Uzzi’s data reveals that ninety per cent of musicals produced during the decade were flops, far above the historical norm. “Broadway had some of the biggest names ever,” Uzzi explains. “But the shows were too full of repeat relationships, and that stifled creativity.”
It’s not that groups aren’t effective generators of creativity. As these studies show, innovation tends to be produced via group processes. Knowledge production is increasingly a collective outcome. Rather than assume that people work best alone, we should think more carefully about what kinds of groups are optimally designed for producing creativity. Diverse groups will be more creative than homogeneous groups. Groups that embrace conflict and critical thought will be less susceptible to groupthink than groups that avoid such conflict. Groups made up of members who have little experience with outsiders will be less creative. I agree with Peter that brainstorming is ineffectively taught in many classrooms, but rather than throw out the idea altogether, we should try to teach people how to design groups that are good at generating new ideas.
- The New Scientist – “Revealed – the capitalist network that runs the world.”
- arXiv blog entry on “econophysicists identify world’s top 10 most powerful companies.”
- The papers on arXiv.
Someone out there must use LinkedIn and know how its networking tools work. If that’s you, I need your help. I’d like to use LinkedIn to show students how to analyze their social network. I know that LinkedIn has its own network mapping tool that lets you visualize your network, but I don’t know if there is a way to export the nodes so that you can do your own analysis of it. I’d really like a way to export the network in a text or excel file. Does anyone know of a way to do this?
[link via David Lazer]
Twitter is getting lots of interest from social scientists. Here’s a piece from the current issue of Science about how “social scientists wade into the tweet stream” (the figure below is from this article). And, an NPR piece on a forthcoming Science article by Macy and Golder on affect and mood and twitter.
Prompted in part by some conversations at the ASA meetings, in part by Gabriel’s discussion of the Social Structures author-meets-critics session, and in part by some gentle prodding from Cosma Shalizi, here’s a current draft of a paper of mine, The Performativity of Networks, that I’ve been sitting on for rather too long. Here’s the abstract:
The “performativity thesis” is the claim that parts of contemporary economics and finance, when carried out into the world by professionals and popularizers, reformat and reorganize the phenomena they purport to describe, in ways that bring the world into line with theory. Practical technologies, calculative devices and portable algorithms give actors tools to implement particular models of action. I argue that social network analysis is performative in the same sense as the cases studied in this literature. Social network analysis and finance theory are similar in key aspects of their development and effects. For the case of economics, evidence for weaker versions of the performativity thesis in quite good, and the strong formulation is circumstantially supported. Network theory easily meets the evidential threshold for the weaker versions; I offer empirical examples that support the strong (or “Barnesian”) formulation. Whether these parallels are a mark in favor of the thesis or a strike against it is an open question. I argue that the social network technologies and models now being “performed” build out systems of generalized reciprocity, connectivity, and commons-based production. This is in contrast both to an earlier network imagery that emphasized self-interest and entrepreneurial exploitation of structural opportunities, and to the model of action typically considered to be performed by economic technologies.
The usual disclaimers about work-in-progress apply.
Via Rense – a visualization of couchsurfing friendships.
Here’s the explanation:
Blue ties represent friendships from outside the organization. Red ties represent friendships formed within the CouchSurfing organization. We have no information about grey ties. The width of tie is proportional with the indicated strength of the friendship: i.e., from “acquaintance” to “best friend.” The movie was done in SoNiA, a highly-recommended free dynamic network visualization tool.