What can 68.4 billion Facebook friendships teach us about reality?

The Facebook Data Team recently published a ground-breaking piece of collaborative research, by Ugander, Karrer, Backstrom, and Marlow,  which characterised the network structure of the entire active population of Facebook as of May 2011.  That’s 721 million active users, who between them had formed 68.4 billion Facebook-friend relationships.   You can read a summary of findings here, and the entire article can be downloaded from here.

Computing the network metrics characterising this population provided employment for 2,250 servers, deployed in a Hadoop cluster.  Awesome™.  Some of it was done on a  a 64 GB machine via a stream-based algorithm, and some on a 24-core 72 GB machine via a novel algorithm.   (Still Awesome™.)

Why did they look at all their data, when doing so was pretty difficult?   Personally, I think they fed their whole active user base into their crunchers partly because they could.  Facebook is not an ordinary engineering company, and it is not just an engineering company, but it is definitely an engineering company.

But the reason given in the text is different:

Network completeness is especially important in the study of online social networks because unlike traditionals social science research , the members of online social networks are not controlled random samples, and instead should be considered biased samples.(p.2)

Now, that is really interesting.    It is interesting not because it’s flawed, but because of the way it’s flawed.   Making the Facebook ‘sample’ as big as humanly possible doesn’t help, if what you want to talk about is the structural characteristics of human social networks.  The whole population of Facebook is a biased sample if you view it as a sample of human social relationships.  The logic of the authors’ assertion that you need to look at the whole population because using just a sample is biased is wrong.   Even if you use the whole population, the entire Facebook universe is still biased as a sample if what you want to talk about is ‘human social networks’.

However, if what you want to talk about is the nature of Facebook-friendships, being able to look at the social graph patterns  from the whole population of active Facebook users is just amazing.   (And, of course, Awesome™.)  But there’s simply no need to confuse Facebook with reality, and say the reason you are looking at your entire data set is to avoid sample bias.  Looking at the whole shooting match doesn’t help.

The authors go on to say:

…the most accurate representation of our social relationships will include as many people as possible.  We are not there yet, but in this paper we characterise the entire social network of active members of Facebook in May 2011… (p. 2)

Again,  a pivotal bit of confusion revealed by choice of language.  The study reveals the structure of Facebook-friendships, and Facebook-social-networks.    It does not study “the entire social network of active members of Facebook”.

The question of how people form and maintain relationships using Facebook is a  truly fascinating one.  So, too,  is the question of how the design of Facebook facilitates and influences these fundamental human processes.   The answers are important for a variety of  reasons, theoretical and applied, personal and commercial.  They are important to a huge range of stakesholders in the system in addition to Facebook itself.  So I’m really glad Facebook thrashed thousands of servers to do this study, and I’m gladder still that they published it.   I am looking forward to cherry-picking some of the tastier findings in future posts.

But I will always have social connections who aren’t connected to me on Facebook.

Social game referral: friends aren’t cheaper, they’re BETTER

I’ve just read a very interesting article, Diffusion dynamics of games on online social networks, from the Usenix conference Workshop on Online Social Networks WOSN 10.   (If you want to read it, too, there’s a .pdf  of it here. )   The paper, by Wei, Yang, Adamic, de Araújo and Rehki,  looks at game invitation behaviour and outcomes  in two different Facebook social games, Yakuza Lords (YL), and Diva Life (DL), both from developer LoLApps.    There is a lot of interesting detail in this work, which I hope to talk more about in future, but the biggest fattest screamingest red-top headline is this:

  • users who were recruited by friends played longer

For both games, recruitment by a friend was not the way that most people started to play the game (an invitation from a friend preceded download for only 37% of new YL players, and 25% for new DL players).   

However those users who were recruited by friends became more engaged.   For example, 80% of non-invited new players left the game within the first day, but over 50% of invited new players played for more than a day.   (Mind you, these are both pretty big loss ratios, but the difference between them is worth paying attention to. )

There are lots of possible reasons why friends make better recruits.  The authors point out several of them.  My own pet hypothesis, which is sitting here wagging its tail at me, is that, for most people,  having people you know ready to play with you with when you first join up makes games more attractive.   

If this is really the case, then there should be an effect on engagement of the size of in-game network you join up to – unless the most important effect is a threshold effect at ’1′.    (Slide rules at dawn, anyone??)    And what’s even more promising, commercially, is a way of identifying, early on, people for whom this is not the case.    Those are the ones you want to reach, when looking beyond your existing user base.

As I’ve said before, I’ve heard a fair amount of vibe-ing that viral marketing on Facebook just ain’t what it used to be.     But  friends of users should still be very highly sought after.   Not, as some people seem to think, because they are free.   Rather, because they are are more valuable.

So much so, that Facebook is offering a way to charge for them, via its Ads for Applications feature.   However it’s worth testing exactly what it yields for you.   One of the study’s many other interesting findings was that although onboarding via a friend invite is a good predictor of engagement,  friend invites sent out en masse had the worst success rate in terms of uptake.

Social graph marketing: I like my friends. But am I like them?

Subaru Six Stars

Image by istargazer via Flickr

According to Facebook’s VP of Partnerships and Platform Marketing, Dan Rose, Facebook’s work with Nielsen shows that social network seeing your friends’ pictures next to a Facebook advertisement leads to “a 60% uptake in brand advertising value” .   I’m not exactly sure what “uptake in brand value” is - and I wasn’t at DLD11, where he made that remark – but the core phenomenon that Rose was talking about isn’t news.   There’s already an aphorism for it dating back, apparently,  to the 1500′s:   Birds of a feather flock together.     (See also, opposites attract…;- )  

The marketing bods version of the ‘birds of a feather’ hypothesis runs something like this:  

People tend to like, and be friends with, people who are similar to them.   People who are similar to each other are similar in many ways, including having similar tastes.    Therefore, your friends are a good source of information about things you might like, not, as you might think, because of what they know about you, but purely because of what they themselves like.     Similarly, knowing what you like is a good predictor of what your friends like – not because you know them well and are sensitive to their needs, but simply because they are your friends, and therefore likely to be similar to you. 

Whatever forces at work here, they are by no means all-powerful.  We have, I am sure,  all given and received presents which are much better barometers of  the giver’s likes than those of the recipient.    I will spare you the details but I recently received a Christmas present that drove this point home to me very strongly.   

But an effect need not be infallible in order to be invaluable.    Do Facebook friends share attributes and preferences, more than you’d expect by chance?  Or more then you’d expect if you knew, say,  basic demographic and psychographic information, but didn’t know ”friend” status? 

To use yet another dodgy hair dye analogy, only Facebook knows for sure.   Facebook, with its knowledge of  its users’ friends, and its knowledge of users’ declared likes, offers a platform which seems tailor-made for exploring the strength, nature, and limits of personal network effects on preferences.   Facebook’s daily operations offer the potential for a large-scale real-time research playground programme of staggering scope and detail.    

Our tendency to be like our friends and our life partners in some ways is a well-documented phenomenon (pop “homophily”, or “assortative mixing”  or “assortative matching” into a search engine if you’d like a quick dip in the surf).   So is the fact that we tend to meet and interact with and become friends with people who are physically close to us.  (Newcombe’s study of this phenomenon in the 1960s seems to have largely held up over time.)    However,  people who are physically close to us may also have been effectively pre-sorted by the universe to  share some of the demographic characteristics important for matching.    So it’s a case of  “not only but also”.

Of course, we do not befriend everyone we have the opportunity to see and interact with frequently.    We can all think of examples, I’m sure, of people we see and interact with every day, who are not  currently  friends, and are unlikely to ever become friends.   No need to name names.   So propinquity, as proximity is sometimes called, is not the whole story.    And neither, of course,  is similarity.

Sit back and think for a moment.  Are you really like your friends?    And is that why you like them?   The answer, probably, is: yes, partly,  in some ways, and no, not always,  in others.   (Ah, the chill wind of common sense.)     Knowing when  friends are likely to be similar to each other in their tastes – and when they aren’t - could be very useful.    Ditto, some knowledge of how strong this effect is, in comparison to other predictive possibilities, helps us to think wisely about what it’s good for, and what it’s not.     But we don’t really know these things in a systematic way – yet.   There are lots of unexplored possibilities in this type of analysis, as well as a large and interesting set of relevant findings from marketing and sociology.  I hope to investigate these issues further in future posts.  For now, let’s just have a little chew on one example. 

I am a Subaru owner.  I believe that I caught this from my sister, who is a happy owner, having done a gruelling daily commute with hers for the last 10 Montreal winters.   I believe that I also passed the Scooby virus on to a friend, who just bought one partly on the strength of my sister’s happiness, and mine.   Contagiousness is highly visible in Subaru-ownership, because of its rarity.    If I bought a Ford, I wouldn’t necessarily be able to trace it back to any particular influence.    But being a Subaru-owner is a niche pleasure, particularly in the UK.    According to one source, only 0.3% of UK new car registrations in December 2010 were Subarus.    

Nonetheless, Subaru is gaining market share.   How?   According to a motor industry guru quoted in a recent Businessweek article,  “They are basically adding people who are Subaru buyers in their hearts, but don’t know it.”    Interesting… 

Although I am a happy Scooby owner, particularly when it is snowing, as it is at this very minute, I am not currently in the market for another Subaru.    (Just as I am Cohen-ed out at present.)    So there isn’t much point marketing Subarus to me.     

But what about my Facebook friends?   They are probably somewhat similar to me, in some ways, as they are my friends.  But they are definitely not similar to me in the sense that none of them own Subarus.  (The gal who bought a Subaru isn’t on Facebook. )  This is pretty much what you would expect, given the rarity of Subaru ownership and the small number of Facebook friends I have.   Even if being my Facebook friend increased your chances of owning a Subaru tenfold, the size of my Friend pool simply isn’t big enough to demonstrate this effect conclusively.

But the interesting question, for Subaru (as well as others),  is whether my Facebook friends more susceptible to Subarus, because they are my friends.  That is to say, are they more susceptible than random people, or than people of similar demographic, psychographic (etc).   

Could my Facebook friends be, as the industry guru put it:  “Subaru buyers at heart, but not know it yet”?   

I don’t know for sure, but my gut feel is some of them are.    That’s certainly the Great Hope of friendship marketing.   It’s possible that Facebook is, even now, figuring out the answer.   Whether friend testimonials work because of some underlying similarity between me and my friends, or because of the trust my friends have in my procurement capabilities, is very much an open question.   But an answerable one.

Similarly, Facebook is undoubtedly working hard on the question of what good my openly declared relationship with Leonard Cohen is as a predictor of my many other susceptibilities.     I’m sure that when they figure it out, they’ll tell me.   (Meanwhile, I’m open to suggestions.)