Who’s more powerful on Facebook – 20 year olds or 60 year olds?

Who is more powerful on Facebook?  60 year olds or 20 year olds?

Image: Flickr - Greenchameleon

I’ve been thinking about this while chewing hard on an interesting nugget from the recent ‘Anatomy of Facebook’ study published by the Facebook Data Team and collaborators.   (You can find a summary of the study here and a link to the full article download here.)     The team selected five different age bands in the Facebook population, and for each of these five age cohorts, they looked at what age all that person’s Facebook friends were.

It’s the oldest Facebookers in the sample which have the widest variation in age range within their circle of FB-friends, and the youngest who have the narrowest.

Figure source: The Anatomy of Facebook - summary (Facebook Data Team)

Does this mean that if you want to achieve the widest potential reach for a message,  your best bet is to target 60 year olds to spread it?   That would be delightfully unexpected, if  true.  But I think it isn’t.

Let’s take a stroll together through a sanity check.  For every 60 year old who’s FB-friends with a 20 year old, there is a corresponding 20 year old in the data who’s FB-friends with that very same 60 year old.   For 60 years, the 20 year olds are noticeable, as a part of their friendship circle.   But,  when you look at the age distribution of FB-friendships for 20 year olds,  60 year olds form a vanishingly small part of their FB-friendship circle.

How can both these things be true?  My guess about what’s going on is that the 20 year olds tend to have many, many more friends than the 60 year olds.  I think that’s the main way in which 20 year olds could be important in 60 year olds’ FB-friends’ age distributions, but 60 year olds are not important (numerically speaking) to 20 year olds. This is just a guess but it’s my best guess about how these two facts could fit together.  If you have other ideas let me know what they are!

What’s the consequence of this pattern of connectivity?   We all know that raw connectivity and influence are not the same thing.  If you’re feeling digressive and a bit geeky here’s a fun paper on the topic of how network structural characteristics affect viral distribution patterns by Kitsak et al.   (Have fun but come back soon.)   But without connectivity, there is no path for influence to propagate.   So connectivity is  interesting.   It’s just not the only thing that’s interesting.

Considering the universe from a path connectivity point of view, what can we say about the relative potential power of 20 year olds vs 60 year olds?   From the point of view of outbound communication,  we can guess that as a 60 year old FB-friend it’s probably pretty hard to get the attention of the 20 year olds you’re connected to.  Speaking purely in connectivity terms, you have to fight for attention against all those inbound comms channels from all those 20 year old age-mates.    Your input is one of many.

But look at the information flow from the opposite perspective, and a tantalising possibility emerges.   The 60 year olds have the most broadly balanced feed, in terms of the age range of their information sources.   They will be better listeners, as their mixing deck is better adjusted to a wider range of signals from reality.   They will know a greater diversity of things.  They will be wiser, for structural reasons.   If you think knowledge is power then you should bet on the 60 year olds.   But I bet you knew that already.


Measurement and analytics for social apps (and elephants)

State fruit - Tomato

Image via Wikipedia

Had a blast at last week’s London Facebook Developer Garage, which I  helped to organise.  Listened to lots of thought-provoking speakers,  including case studies from European social game studios Sharkius, and Weka Entertainment.   Kontagent presented from Seattle, via Skype –  and the connection didn’t flake out once.

I gave a talk, too, test-piloting a framework I’ve been thinking about for understanding measurement and analytics for social applications.   Invited the audience to throw tomatoes at me but fortunately none of them had come prepared.

Loads of fun (oooh, how I love giving talks!!), with only one big WTF moment –  my .ppt slides, so lovingly prepared in Office 2002, went all shy and invisible when viewed in a modern version on the Official Laptop Which Ran All Presentations.    (Perhaps the audience were too busy wondering what I was on about to throw projectiles…!)

Anyhow, here’s the good news.    The slides from my talk, Measurement and Analytics for Social Apps – Understanding your Elephant are now up on slideshare.

Fun fact: Slideshare is better at converting .ppt than Microsoft is,  so even if you are keeping up with the upgrade treadmill, you should be able to see them.

Enjoy.   Best viewed with feta, red onion, and olive oil.

Facebook connection targeting: which friends are the best?

My friends aren’t all the same.   And neither are yours, I bet.   Same thing when it comes to social graph marketing:  some friends are better than others at inspiring friends to action.    The question is- which ones?

Right now, there’s a lot of interest in methods for effectively identifying ‘influencers’ based on identifiable characteristics, such as their connectivity in a social network.   The question isn’t a new one  – my gut feeling is that it is as old as human communal life.    However, what is new is that online social networks provide the means, motive and opportunity for understanding these effects at scale, and putting them to work.    Hence the interest.

I’ve written recently about the findings of a recent study by Wei, Jang, Adamic, de Araújo and Rehki that looked at social game invitations and outcomes in two popular Facebook games from LOLApps, Diva Life and Yazuka Lords.  The paper is called Diffusion dynamics of games on online social networks, and it was presented last year at the Usenix conference Workshop on Online Social Networks WOSN 10.   (If you want to read it directly,  there’s a .pdf  of it here. ).    The headline result was that players recruited via friends were more engaged, and played longer.

But there is a lot of tantalising information in the article about what type of invitation behaviour is the most successful.   Here are some of the key results from this part of the work:

  • people who have more friends invite more friends
  • the success rate for invitations decreases strongly as more invitations are sent
  • the success rate for invitations decreases strongly as the number of invitations sent in one batch rises, controlling for the total number of successful invitations made

Very clearly, there is a story to be told here about more selective invitations being more effective, when measured per invitation.    But there is more than one story that can be told about what this means in practice, for designers.

There are also lots of potentially interesting stories that didn’t make it into the paper.   For one thing – it’s not clear whether the success rate for invitations varies with the proportion of friends invited, as well as with their absolute number.    It seems likely that there would be a difference in outcomes between someone with 500 friends inviting 5 friends, someone with 50 friends inviting 5 friends, and someone with 5 friends inviting 5 friends.     Similarly, someone with 500 friends who invites 50 friends is quite a different type of fish from the person with 50 friends who invites 50 friends.     Why does it matter?   It matters because you’d expect the success rate to be different for these cases, and success rate, rather than the total amount of success, is increasingly important (for reasons I will explain later).

Before we mope too much about what we don’t know as a result of this work, here is something we do know:

  • 10% of users are responsible for 50% of successful invitations

Wow.   That’s pretty much all I can say about it.  Except maybe awesome (which, it should be noted, I said with an entirely straight face 😐 ).

We’re not told in the article whether these top performing ‘salespeople’ are more accurate, or more prolific – or both.  We don’t know what their success rate is.  And that’s a very important question.

Are the top 10% responsible for their fair share of unsuccessful invitations, or are they successful because they are so prolific it doesn’t matter if they are even averagely effective?    My guess – and it is purely that, a guess – is that numbers game at the moment is such that super-promiscuous inviters, who have lots of friends, and invite them indiscriminately, and repeatedly, give the highest absolute return in terms of new eyes on screens, despite being the least efficient.    Kind of like the person we have all met who is successful in making conquests  because he (or she) really doesn’t care about failures, only about successes.

Targeting all of a person’s friends will give the ‘best result’, in that it will give the highest number of responses  –  but this approach isn’t cost-free, for a number of reasons:

  • Opportunity cost.   In exposing someone to an offer they are not interested in, an opportunity is wasted.  This opportunity cost is in fact always partly borne by the initiator of the action – it’s just not always obvious.     (What if they could have sold the opportunity to someone who could make use of it?)
  • Annoyance.    In exposing someone to an offer they are not interested in, you might lose the ability to attract them, at a later time,  to an offer they are interested in.
  • Real direct cost.    It’s nice if you can get your users to do your marketing for you, but it’s increasingly the case that you need to get your shovel out and help, too.   When you pay to target your users’ friends, what are you getting for your money?   Do you want all of them, or just some of them?

As connection targeting increasingly becomes a paid-for service, all these types of costs, direct, indirect, and opportunity cost will come under increasing scrutiny.

Let’s look at the study’s results from the other end of the lens for a moment.  If you are like the people in the study (and odds are, you are, as there were millions of users involved), your best friends, from an invitation point of view – i.e. the ones who issue invitations that you are most likely to accept – come from friends who send relatively few invitations,  and send them incrementally, but persistently.     One interpretation of this finding is that these type of invitations are ones that result from friends using their own intelligence and applying it to the developer’s problem.     It may become more important to explicitly encourage this type of accuracy, and value-add, by the type of invitation which is made, and how it is monitored and managed.

What’s the take-away?    Unfortunately, you can’t simply grab the first result you run across, and ride off into the sunset with it whooping and hollering with joy.     You might end up riding in the wrong direction.   After all, the type of product that is being recommended has an influence how people behave when recommending it, as shown by Leskovec, Singh and Kleinberg in their 2006 paper, Patterns of Influence in a Recommendation Network.        And there are other things to think about too.

However, there is one moral that can be easily squeezed out of the results, which is that there is a lot to learn from asking the question.  You don’t ask, you don’t get applies to behavioural insights as well as to lots of other things in life.