Data Science of the Facebook World

April 24, 2013

More than a million people have now used our Wolfram|Alpha Personal Analytics for Facebook. And as part of our latest update, in addition to collecting some anonymized statistics, we launched a Data Donor program that allows people to contribute detailed data to us for research purposes.

A few weeks ago we decided to start analyzing all this data. And I have to say that if nothing else it’s been a terrific example of the power of Mathematica and the Wolfram Language for doing data science. (It’ll also be good fodder for the Data Science course I’m starting to create.)

We’d always planned to use the data we collect to enhance our Personal Analytics system. But I couldn’t resist also trying to do some basic science with it.

I’ve always been interested in people and the trajectories of their lives. But I’ve never been able to combine that with my interest in science. Until now. And it’s been quite a thrill over the past few weeks to see the results we’ve been able to get. Sometimes confirming impressions I’ve had; sometimes showing things I never would have guessed. And all along reminding me of phenomena I’ve studied scientifically in A New Kind of Science.

So what does the data look like? Here are the social networks of a few Data Donors—with clusters of friends given different colors. (Anyone can find their own network using Wolfram|Alpha—or the SocialMediaData function in Mathematica.)

social networks

So a first quantitative question to ask is: How big are these networks usually? In other words, how many friends do people typically have on Facebook? Well, at least for our users, that’s easy to answer. The median is 342—and here’s a histogram showing the distribution (there’s a cutoff at 5000 because that’s the maximum number of friends for a personal Facebook page):

distribution of number of friends for our users

But how typical are our users? In most respects—so far as we can tell—they seem pretty typical. But there are definitely some differences. Like here’s the distribution of the number of friends not just for our users, but also for their friends (there’s a mathematical subtlety in deriving this that I’ll discuss later):

distribution of number of friends for users+friends

And what we see is that in this broader Facebook population, there are significantly more people who have almost no Facebook friends. Whether such people should be included in samples one takes is a matter of debate. But so long as one looks at appropriate comparisons, aggregates, and so on, they don’t seem to have a huge effect. (The spike at 200 friends probably has to do with Facebook’s friend recommendation system.)

So, OK. Let’s ask for example how the typical number of Facebook friends varies with a person’s age. Of course all we know are self-reported “Facebook ages”. But let’s plot how the number of friends varies with that age. The solid line is the median number of friends; successive bands show successive octiles of the distribution.

number of friends vs. age

After a rapid rise, the number of friends peaks for people in their late teenage years, and then declines thereafter. Why is this? I suspect it’s partly a reflection of people’s intrinsic behavior, and partly a reflection of the fact that Facebook hasn’t yet been around very long. Assuming people don’t drop friends much once they’ve added them one might expect that the number of friends would simply grow with age. And for sufficiently young people that’s basically what we see. But there’s a limit to the growth, because there’s a limit to the number of years people have been on Facebook. And assuming that’s roughly constant across ages, what the plot suggests is that people add friends progressively more slowly with age.

But what friends do they add? Given a person of a particular age, we can for example ask what the distribution of ages of the person’s friends is. Here are some results (the jaggedness, particularly at age 70, comes from the limited data we have):

friend ages for people of different ages

And here’s an interactive version, generated from CDF:

 

The first thing we see is that the ages of friends always peak at or near the age of the person themselves—which is presumably a reflection of the fact that in today’s society many friends are made in age-based classes in school or college. For younger people, the peak around the person’s age tends to be pretty sharp. For older people, the distribution gets progressively broader.

We can summarize what happens by plotting the distribution of friend ages against the age of a person (the solid line is the median age of friends):

median age of friends vs. age

There’s an anomaly for the youngest ages, presumably because of kids under 13 misreporting their ages. But apart from that, we see that young people tend to have friends who are remarkably close in age to themselves. The broadening as people get older is probably associated with people making non-age-related friends in their workplaces and communities. And as the array of plots above suggests, by people’s mid-40s, there start to be secondary peaks at younger ages, presumably as people’s children become teenagers, and start using Facebook.

So what else can one see about the trajectory of people’s lives? Here’s the breakdown according to reported relationship status as a function of age:

relationship status fractions vs. age

And here’s more detail, separating out fractions for males and females (“married+” means “civil union”, “separated”, “widowed”, etc. as well as “married”):

relationship status fractions vs. age

There’s some obvious goofiness at low ages with kids (slightly more often girls than boys) misreporting themselves as married. But in general the trend is clear. The rate of getting married starts going up in the early 20s—a couple of years earlier for women than for men—and decreases again in the late 30s, with about 70% of people by then being married. The fraction of people “in a relationship” peaks around age 24, and there’s a small “engaged” peak around 27. The fraction of people who report themselves as married continues to increase roughly linearly with age, gaining about 5% between age 40 and age 60—while the fraction of people who report themselves as single continues to increase for women, while decreasing for men.

I have to say that as I look at the plots above, I’m struck by their similarity to plots for physical processes like chemical reactions. It’s as if all those humans, with all the complexities of their lives, still behave in aggregate a bit like molecules—with certain “reaction rates” to enter into relationships, marry, etc.

Of course, what we’re seeing here is just for the “Facebook world”. So how does it compare to the world at large? Well, at least some of what we can measure in the Facebook world is also measured in official censuses. And so for example we can see how our results for the fraction of people married at a given age compare with results from the official US Census:

fraction married vs. age

I’m amazed at how close the correspondence is. Though there are clearly some differences. Like below age 20 kids on Facebook are misreporting themselves as married. And on the older end, widows are still considering themselves married for purposes of Facebook. For people in their 20s, there’s also a small systematic difference—with people on Facebook on average getting married a couple of years later than the Census would suggest. (As one might expect, if one excludes the rural US population, the difference gets significantly smaller.)

Talking of the Census, we can ask in general how our Facebook population compares to the US population. And for example, we find, not surprisingly, that our Facebook population is heavily weighted toward younger people:

population vs. age

OK. So we saw above how the typical number of friends a person has depends on age. What about gender? Perhaps surprisingly, if we look at all males and all females, there isn’t a perceptible difference in the distributions of number of friends. But if we instead look at males and females as a function of age, there is a definite difference:

number of friends vs. age

Teenage boys tend to have more friends than teenage girls, perhaps because they are less selective in who they accept as friends. But after the early 20s, the difference between genders rapidly dwindles.

What effect does relationship status have? Here’s the male and female data as a function of age:

median number of friends vs. age

In the older set, relationship status doesn’t seem to make much difference. But for young people it does. With teenagers who (mis)report themselves as “married” on average having more friends than those who don’t. And with early teenage girls who say they’re “engaged” (perhaps to be able to tag a BFF) typically having more friends than those who say they’re single, or just “in a relationship”.

Another thing that’s fairly reliably reported by Facebook users is location. And it’s common to see quite a lot of variation by location. Like here are comparisons of the median number of friends for countries around the world (ones without enough data are left gray), and for states in the US:

median number of friends by location

There are some curious effects. Countries like Russia and China have low median friend counts because Facebook isn’t widely used for connections between people inside those countries. And perhaps there are lower friend counts in the western US because of lower population densities. But quite why there are higher friend counts for our Facebook population in places like Iceland, Brazil and the Philippines—or Mississippi—I don’t know. (There is of course some “noise” from people misreporting their locations. But with the size of the sample we have, I don’t think this is a big effect.)

In Facebook, people can list both a “hometown” and a “current city”. Here’s how the probability that these are in the same US state varies with age:

percentage who moved states vs. age

What we see is pretty much what one would expect. For some fraction of the population, there’s a certain rate of random moving, visible here for young ages. Around age 18, there’s a jump as people move away from their “hometowns” to go to college and so on. Later, some fraction move back, and progressively consider wherever they live to be their “hometown”.

One can ask where people move to and from. Here’s a plot showing the number of people in our Facebook population moving between different US states, and different countries:

migration between US states

migration between countries

There’s a huge range of demographic questions we could ask. But let’s come back to social networks. It’s a common observation that people tend to be friends with people who are like them. So to test this we might for example ask whether people with more friends tend to have friends who have more friends. Here’s a plot of the median number of friends that our users have, as a function of the number of friends that they themselves have: median friend count vs. friend count

And the result is that, yes, on average people with more friends tend to have friends with more friends. Though we also notice that people with lots of friends tend to have friends with fewer friends than themselves.

And seeing this gives me an opportunity to discuss a subtlety I alluded to earlier. The very first plot in this post shows the distribution of the number of friends that our users have. But what about the number of friends that their friends have? If we just average over all the friends of all our users, this is how what we get compares to the original distribution for our users themselves:

distribution of number of friends

It seems like our users’ friends always tend to have more friends than our users themselves. But actually from the previous plot we know this isn’t true. So what’s going on? It’s a slightly subtle but general social-network phenomenon known as the “friendship paradox”. The issue is that when we sample the friends of our users, we’re inevitably sampling the space of all Facebook users in a very non-uniform way. In particular, if our users represent a uniform sample, any given friend will be sampled at a rate proportional to how many friends they have—with the result that people with more friends are sampled more often, so the average friend count goes up.

It’s perfectly possible to correct for this effect by weighting friends in inverse proportion to the number of friends they have—and that’s what we did earlier in this post. And by doing this we determine that in fact the friends of our users do not typically have more friends than our users themselves; instead their median number of friends is actually 229 instead of 342.

It’s worth mentioning that if we look at the distribution of number of friends that we deduce for the Facebook population, it’s a pretty good fit to a power law, with exponent -2.8. And this is a common form for networks of many kinds—which can be understood as the result of an effect known as “preferential attachment”, in which as the network grows, nodes that already have many connections preferentially get more connections, leading to a limiting “scale-free network” with power-law features.

But, OK. Let’s look in more detail at the social network of an individual user. I’m not sufficiently diligent on Facebook for my own network to be interesting. But my 15-year-old daughter Catherine was kind enough to let me show her network:

social network

There’s a dot for each of Catherine’s Facebook friends, with connections between them showing who’s friends with whom. (There’s no dot for Catherine herself, because she’d just be connected to every other dot.) The network is laid out to show clusters or “communities” of friends (using the Wolfram Language function FindGraphCommunities). And it’s amazing the extent to which the network “tells a story”. With each cluster corresponding to some piece of Catherine’s life or history.

Here’s a whole collection of networks from our Data Donors:

social networks

No doubt each of these networks tells a different story. But we can still generate overall statistics. Like, for example, here is a plot of how the number of clusters of friends varies with age (there’d be less noise if we had more data):

mean number of clusters vs. age

Even at age 13, people typically seem to have about 3 clusters (perhaps school, family and neighborhood). As they get older, go to different schools, take jobs, and so on, they accumulate another cluster or so. Right now the number saturates above about age 30, probably in large part just because of the limited time Facebook has been around.

How big are typical clusters? The largest one is usually around 100 friends; the plot below shows the variation of this size with age:

median size of largest cluster vs. age

And here’s how the size of the largest cluster as a fraction of the whole network varies with age:

relative size of largest cluster vs. age

What about more detailed properties of networks? Is there a kind of “periodic table” of network structures? Or a classification scheme like the one I made long ago for cellular automata?

The first step is to find some kind of iconic summary of each network, which we can do for example by looking at the overall connectivity of clusters, ignoring their substructure. And so, for example, for Catherine (who happened to suggest this idea), this reduces her network to the following “cluster diagram”:

cluster diagram of social network

Doing the same thing for the Data Donor networks shown above, here’s what we get:

mini social networks

In making these diagrams, we’re keeping every cluster with at least 2 friends. But to get a better overall view, we can just drop any cluster with, say, less than 10% of all friends—in which case for example Catherine’s cluster diagram becomes just:

cluster diagram after clusters with less than 10% of friends were dropped

And now for example we can count the relative numbers of different types of structures that appear in all the Data Donor networks:

Bar chart of different types of clustered social networks

And we can look at how the fractions of each of these structures vary with age:

community graph makeup vs. age

What do we learn? The most common structures consist of either two or three major clusters, all of them connected. But there are also structures in which major clusters are completely disconnected—presumably reflecting facets of a person’s life that for reasons of geography or content are also completely disconnected.

For everyone there’ll be a different detailed story behind the structure of their cluster diagram. And one might think this would mean that there could never be a general theory of such things. At some level it’s a bit like trying to find a general theory of human history, or a general theory of the progression of biological evolution. But what’s interesting now about the Facebook world is that it gives us so much more data from which to form theories.

And we don’t just have to look at things like cluster diagrams, or even friend networks: we can dig almost arbitrarily deep. For example, we can analyze the aggregated text of posts people make on their Facebook walls, say classifying them by topics they talk about (this uses a natural-language classifier written in the Wolfram Language and trained using some large corpora):

topics discussed on Facebook

Each of these topics is characterized by certain words that appear with high frequency:

word clouds for topics discussed on Facebook

And for each topic we can analyze how its popularity varies with (Facebook) age:

topics discussed on Facebook

It’s almost shocking how much this tells us about the evolution of people’s typical interests. People talk less about video games as they get older, and more about politics and the weather. Men typically talk more about sports and technology than women—and, somewhat surprisingly to me, they also talk more about movies, television and music. Women talk more about pets+animals, family+friends, relationships—and, at least after they reach child-bearing years, health. The peak time for anyone to talk about school+university is (not surprisingly) around age 20. People get less interested in talking about “special occasions” (mostly birthdays) through their teens, but gradually gain interest later. And people get progressively more interested in talking about career+money in their 20s. And so on. And so on.

Some of this is rather depressingly stereotypical. And most of it isn’t terribly surprising to anyone who’s known a reasonable diversity of people of different ages. But what to me is remarkable is how we can see everything laid out in such quantitative detail in the pictures above—kind of a signature of people’s thinking as they go through life.

Of course, the pictures above are all based on aggregate data, carefully anonymized. But if we start looking at individuals, we’ll see all sorts of other interesting things. And for example personally I’m very curious to analyze my own archive of nearly 25 years of email—and then perhaps predict things about myself by comparing to what happens in the general population.

Over the decades I’ve been steadily accumulating countless anecdotal “case studies” about the trajectories of people’s lives—from which I’ve certainly noticed lots of general patterns. But what’s amazed me about what we’ve done over the past few weeks is how much systematic information it’s been possible to get all at once. Quite what it all means, and what kind of general theories we can construct from it, I don’t yet know.

But it feels like we’re starting to be able to train a serious “computational telescope” on the “social universe”. And it’s letting us discover all sorts of phenomena. That have the potential to help us understand much more about society and about ourselves. And that, by the way, provide great examples of what can be achieved with data science, and with the technology I’ve been working on developing for so long.

Join the Discussion
Join the Discussion

49 more comments. Show all »

  1.  

    superb – your friends abroad live wehre you’d go if you’d emigarte?

  2.  

    Beautiful visualizations. As you said – some of these data patterns are not surprising at all. I am skeptical of FB data in general though – it seems “low quality” to me. FB data lacks sincerity, most is perishable (very little value beyond real-time), and vanity plays a huge factor.

    Twitter data is actually more intriguing to me, despite the lack of social context.

  3.  

    I’d just like to thank you for making Mathematica. I’ve just discovered it. Haven’t even installed it. I’m sorting through the documentation, watching tutorials and otherwise defining how I plan on using it when I activate my free tutorial. I have spent quite a bit of time alone figuring out what I want to do with my mind and life and it is so refreshing and invigorating to not have to build the tools I am going to need from scratch. Again thank you so much. Thank you for the time, effort, and, I am certain, frustration.

  4.  

    Really fascinating, thanks for posting!

  5.  

    Hi Stephen. I think you will find that Filipinos really are among the most social people in the world! Everyone is a “friend”. No matter how short the relationship has been.

    Luis
  6.  

    This was excellent!

    Josh
  7.  

    Hii mr Wolfram I find your study very interesting and as you said yourself maybe a little stereotyped regarding interests and your age. but as you I was not that surpriced just a litte baffled that it is so easy too read society. A small comment: it could be interesting too do this survey in different communities around the world and see how much local culture would affect the results.
    This is just a guess: you talked about rural areas usually shows less friends like mid or west U.S.A and you mentioned Iceland as a surprice. I was not surpriced by Iceland I was sure before I even read the diagrams that they would have a high numbers of f-friends. My theory: they are so isolated, but still they are very educated people . They have more bookstores per capita and they wright more books per capita than any other country in the world. and this is only my opinion or feeling there are very openminded, they travel a lot.There population is only 3.22.000 Example I travelled arond the world and I met more Icelandic people around the world in very remote areas than I met Americans ( population approx. 322 millions) or canadians. the same count for Swedish and a little less for Australians. Compare too people in all of these countries they travel a lot which means they meet alot off people where facebook is the easyest way off keeping in touch. the same counts for Norway, Holland and Sweden. if i’m right Denmark has almost 4.000.000 Facebook users with only only a population of approx. 5.600.000. so my point is the more welltravelled peole are the more they use social networks. i might be wrong but i really think it’s so interesting that it’s worth too put in too consideration regarding worldwide survey off facebook users. al the best Stefan Candby Petersen

    Stefan Candby Petersen
  8.  

    Great,Fantastic,Awesome,Beautiful explanation For Data Science of Facebook.

    Mohamed Ali
  9.  

    Would you create Personal Analytics for regional social networks like VK and Orkut to remove USA bias from your statistic?

    Kirill
  10.  

    Very intriguing, great analysis!

    Noah
  11.  

    *applauds* :)

  12.  

    Fun read, thanks!

    Dara
  13.  

    This post is just.. beautiful!

  14.  

    Very interesting analysis. One small thing that caught my eye, however, was the perspective typified by this statement: “People talk less about video games as they get older, and more about politics and the weather.” I realize that this is ambiguous in a way, but it suggests that “people” have a uniform behavior, while with the major growth in Facebook occurring over the last decade, this is more of a snapshot of several generations. So it’s very hard to extrapolate trends, I think, considering the graph is composed of such drastically different populations! We must be careful not to anthropomorphize data from a range of inputs into a single typical unit’s “journey” through that data, as it is not indicative of that. At any rate, thanks for your time and insight.

  15.  

    Congratulations, clear analysis.
    Would be possible to share the code for the embedding graph of Countries -flags??
    It’s impossible find it some similar in mathematica books.
    Thanks

    Carlos
  16.  

    thanks for sharing this. weather and politics look like the topics which grow the most with age. sounds about right!

  17.  

    Could you make and interactive slide from the world map, and also moving stats?

    - Russia and China have other social medias more popular.
    - I would say Germany, France, Spain are more sceptic about FB for some reason (maybe privacy, one company dominace), compared to Nordic countries for example.

    Terveisiä Suomesta
  18.  

    Very good work!

    JCesar
  19.  

    It was fairly easy, from inspection, to intuit that *something* like a power law was involved in so many of the distributions. However, I think that, on closer examination, near relatives are somewhat more explanatory, in that the stochastic processes that generate them have been used for some time to characterize “Casual Groups of Monkeys and Men” (to cite indirectly just one publication). Discrete distributions, such as the Negative Binomial and the Logarithmic, have been long used in these works. It is also possible to decompose such distributions into Poisson components, given that one may not really know how to count “individuals” (pairs, triads, etc. may actually count as 1, in certain circumstances). All this supposes that you do not believe “The World” is lognormal – pace Tukey.

    Reid Kaplan
  20.  

    Great Work!

  21.  

    Hi Stephen,
    Would it be possible to draw a plot of “facebook usage” versus age? Maybe frequency of posts+ likes versus age or That would offer some great insight as well. This is amazing stuff. It would also be great to look at this same information ten years from now to see if their are longitudinal insights about society (are we getting married later, having kids later, how does the trend change when someone is ‘born into’ facebook or has a profile from age 5 onwards look like) and so on.

  22.  

    I don’t think it is possible to say looking at this data that people changed their behavior and interests as they get older. This is because computers and facebook hasn’t been around for that long.
    Take a look at video games for example. Mostly young people play them and perhaps they will continue to do so when they grow older. Right now you have data from older people who did not grow up with video games and probably never tried them. It is not as if they once played them and now lost interest. Same goes for other subjects.

    Louise Chanarý
  23.  

    Who were the major contributors who did the analyses shown here? (I’m assuming it wasn’t just the author of the blog…)

    Andrew Sornborger
  24.  

    Fantastic visuals! Do you mind sharing what toolkit you used? D3.js?

    DR
  25.  

    What utter beauty.. This is really moving, it is not just a cool little facebook analysis. This is the sort of long-term understanding of human society and life that has hitherto been dealt with primarily by religious orders.

    Atty
  26.  

    The large dispersion in the count of friends per user appears to be highly correlated with the proportion of the population who could possibly be friends of that person who are also members of Facebook. Thus, it may be possible to correct for this by estimating the number of friends a user would have if Facebook membership was at 100%. Such an analysis might stratify the sample by age, gender, marital status, income, education, urban density, and locale/language.

  27.  

    Great visuals and detailed explanation

  28.  

    This is absolutely fantastic. While I agree that there are issues with Facebook data integrity, I suspect that there is are some high-level truths here. My one take away is that the data lend themselves to the possibility of more accurate valuations when forecasting the growth of social networks. For example, if you know that network X is users in a certain age group with friend equivalent connection. You can estimate the network’s growth using the friend distributions presented here. Its not overly tight, but it offer yet another level of insight. Just think what can be done to making the case for targeted marketing programs?

  29.  

    Outstanding REPOSITIONING of DESCRIPTIVE STATISTICS as the MOST powerful profession and SCIENTIFIC DISCIPLINE in the age of Big Data.

    I thank you for that contribution of yours, one in along line of the BEST you as a Scientist bestowed the Scientific community with for four dacedes.

    Congratulations for another MASTERPIECE.

    Aviva Lev-Ari, PhD
    Http://pharmaceuticalintelligence.com

    Aviva Lev-Ari, PhD
  30.  

    Great stuff, but hand on heart I feel I would have got the distribution patterns of three quarters of these correct without data mining. I guess people and their demographics are about the most powerful indicators available. Very interesting the huge drop off in the age profile of Facebook. Does that suggest an eventual decline as Facebook users age and Facebook becomes less appealing?

    Chris Robinson
  31.  

    Mr. Wolfram, Very interesting stuff. Like you, I am interested in “lifecycle stories” of people. One aspect of your FB data that I’d like to see is a correlation between iconic network summary and representative word clusters of comments by other people about the person whose friend network it is. In other words, for each of the iconic network structures in your periodic table, are there distinctive word clusters that “self define” that person of that particular network structure. In other words, what distinguishes the person who has two main clusters of friends, from the person who has three main clusters, four main clusters, etc. ???? My thought is that this self definition would show up, not necessarily in the texts of POSTS by the person, but in the COMMENTS by the person’s friends, and ideally, by those comments that explicitly reference the person.

  32.  

    How about military? There is a page for my ship, for example, where I have connected with some of the people I served with and some who just had the same duty station at different times.

    Matt
  33.  

    Mr. Wolfram,
    in the second sentence under the last diagrams you wrote: “People talk less about video games as they get older…”.
    I think you could only say this, if you compare this analysis with other analysis from years before (like 10, 20, 30 years before). Now you are watching at a fixed moment of time (i suppose). I think the only vaild conclusin from the diagrams is: “Older people talk less about video games than younger,.. ”

    So i hope you will repeat this analysis every 5 years or so… then we will probably see the dynamics of the society, not only the static description of this moment.

    And i didnt lookup the original data, but i would love to hear how the dataset you analysed was build.

    At last, but not least, thanks for the nice work.

    Kai Werthwein

    Kai Werthwein
  34.  

    Amazing analysis.
    Great insights. Very interesting to see what people talk about ’bout certain topics. Also the ages of friends is widening by the age people are getting is very interesting. You clearly see the impact of getting older and growing a family with a broader sets of ages with a fewer set of friends.
    Translates the real world.

  35.  

    Hi Stephen, great stuff. It rarely suprising, but it’s a fundus for marketers to define their target audiences. Thx!

  36.  

    This is the first time I find anything interesting about Facebook.
    And the combined pattern of graphs are gorgeous.
    Great job! And thank you.

    Quentin Choob
  37.  

    Awesome, Fantastic Analysis.

    Maurice
  38.  

    Amazing. Simply amazing.

    Adam P
  39.  

    Wow, this is definitely the most dense information on statistics about social networks I’ve ever found. Thank you for posting it.

  40.  

    This is really interesting stuff! Our online “social networks” may be only an approximation of our real social networks but it seems they can say a lot about our lives.

    One thing that has me curious is the question of whether one could carve out closer, tighter communities within those larger communities. For example, looking at my network shows the community of the small (~650 total) school I currently attend, but it’s impossible to see the smaller communities. On average each of us is friends with about 200 of the others, so the graph is very strongly connected and it’s hard to pick out smaller groups. Through more personal interactions, though (such as commenting on or liking posts, being mutually tagged in photos or statuses, messaging each other with some frequency) I wonder if it would be possible to find the subcommunities – the friends I run with, the friends on my hall, the friends that take similar classes to mine based on mutual academic interests, etc. It seems there’s a lot of potential for sociology research in social network analysis, but only to the degree that people are willing to donate their digital data.

    David Spencer
  41.  

    This is superb! Thanks a lot for the article!

    Yogesh
  42.  

    >> But quite why there are higher friend counts for our Facebook
    >> population in places like Iceland
    Couldn’t it be partially the climate, and partially that most people in Iceland are related to each other?

  43.  

    Great post and beautiful visualizations. Very useful material for anyone interested in studying social media as well as data visualization in general. I blogged about it here: http://deepestturtle.quora.com/Stephen-Wolframs-Post-on-Analysis-of-Facebook-Data. Thanks.

  44.  

    This was very well-articulated and I like the visualizations!

    I wonder how a similar analyses would look on LinkedIn data ?

    Amar Rajasekhar
  45.  

    The next time we fire a pioneer spacecraft out of the solar system, it should have a gold anodised aluminium plaque with this wonderful blog post etched onto it to tell the aliens what humans are all about. We might not expect first contact for a while though: “So… these humans…they like sports and birthdays? Hmm.”

    Rich Clarke
  46.  

    I was just wondering what the distance between the dots in a cluster of friends means?

    Lukasz Webski
  47.  

    High FB saturation in Philippines as well as a low average population age and a propensity to engage in social games will result in higher average # of friends.

    Matthew
  48.  

    Absolutely great post. I love it. I wouldmlike to adapt this technique t0for analyzing researcher networks!

    aidis
  49.  

    Fascinating. I’m going to spend a lot more time mulling your info. And may I point out that your data assumes life ends at 70–even sooner on some of your charts? I am 80, dude, and last time I looked there were 1,418 Friends for my personal FB page and 1,492 for a page I manage: Giraffe Heroes. And they’re not the same people. To quote Monty Python–Not dead yet!

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