These days we are all a bit social on-line. We have always been social creatures, but now we have technology to help us manage those connections, from well-known sites like Facebook, YouTube, and Digg, to photo sharing sites like Flickr , even virtual world applications such as Second Life.

Computer technology means we can start to map out the relationships an individual has. Certainly we could have done this in the past with paper and pen, but applications like Twitter, Linked-In and Facebook mean that a vast swathe of the population are now submitting details of their relationships into databases, where they can be graphed and modelled by computer. This idea of a social graph – a map of relationships that individuals have with each other – has applications in both business and consumer marketing.

In the early days of the Internet it was joked that “on The Internet nobody knows you are a dog”. However, on today’s Internet we do know who you are, what you do, and the relationships that you have. Depending on your privacy settings, this information is available to a narrower or broader set of people – but it is, nonetheless, available.

One of the most interesting things about making a graph of the relationships is looking at the connections of our connections. Taking an obvious example: I know a few people, those people know other people. With a social graph (or with social media applications) I can see that two of my friends don’t know each other, but they do know a third mutual acquaintance. That creates new ways of introducing people to each other, and strengthening relationships with mutual contacts (see the Dunbar’s Number – Groups, Language and Social Media post with reference to tribes and clans in this context).

In some ways there’s nothing new there. Social people have been doing this sort of thing for millenia, but what is new is that people can use computer technology to identify friends or contacts that might be relevant with information that might previously have been missed or unnoticed. I have two friends who live at the opposite ends of the country, who I’ve never seen at the same time, and I assumed never knew each other. It wasn’t until Facebook came on the scene that I realised they went to the same school as children. A detail that might not (and in fact did not) come up in years of conversation.

This idea is codified in the concept of friends of a friend or “FOAF“. It was an early attempt to capture a person’s social graph and publish it on the web. The idea is that I could embed a list people that I know on my web site, so that you can see who you know too – that way you can see if you are a friend of a friend. One hop away on the social graph. By identifying those mutual contacts it provides a way for us to come to know each other. That is the concept that business social networking site LinkedIn is based upon.

So how does FOAF work? It uses something called RDF – Resource Description Framework – to express metadata, that is information about information. In the case of FOAF that is information about people and their interests, relationships and actitivites.

FOAF uses RDF to express metadata about people, and their interests, relationships and activities. Founded by Dan Brickley and Libby Miller, FOAF is an open community-lead initiative which is tackling head-on the wider Semantic Web goal of creating a machine processable web of data.

Semantic web overlays data about data on the Internet so that computers can make sense of it. Because FOAF information is machine readable, computer applications can read it and process it to present information or bring things to our attention. So a very simple and idealistic view might be you come to my website or my page on Facebook and the web brouser automatically picks up that FOAF information and is able to notifiy you that there are some people we know in common. The idea is to build that information into all sorts of web pages so that many applications become, as it were, social or at least socially aware.

So FOAF, in the technical sense, is a very simple text structure, based on an XML format, which is machine and human readable – although not too pretty for a human. It is very easy to write applications to use it. A FOAF entry might include information such as my name, gender, title, what my preferred nickname is, separate out my family name, point to my home page or my blog, and include similar information for my contacts. It is a very simple piece of data, but we can add details about the nature of the relationships. It isn’t as complex as it probably sounds, it is flat text, which might look a bit like this:

<foaf:Person>
   <foaf:name>Benjamin Ellis</foaf:name>
   <foaf:gender>Male</foaf:gender>
   <foaf:title>Mr</foaf:title>
   <foaf:givenname>Benjamin</foaf:givenname>
   <foaf:family_name>Ellis</foaf:family_name>
   <foaf:nick >jamin</foaf:nick>
   <foaf:mbox_sha1sum>...(inverse functional property)...</foaf:mbox_sha1sum>
   <foaf:homepage rdf:resource="http://www.benjaminellis.co.uk"/>
   <foaf:weblog rdf:resource="http://www.redcatco.com/blog/"/>
   <foaf:workplacehomepage rdf:resource="http://redcatco.com/" />
   <foaf:depiction
           rdf:resource="http://benjaminellis/images/bmje.jpg" />
   <foaf:knows>
       <foaf:Person>
         <foaf:name>Joe Blogs</foaf:name>
       </foaf:Person>   
   </foaf:knows>
 </foaf:Person>

What does all this technology do? It give us opportunities to introduce people to other people, or to find people via mutual contacts. It might be computer-based, but the end goal is human to human social interaction. The power of my social graph, the map of my relationships, is not just in the releationsips I have, but also in that friend of a friend information – The relationships my contacts have, and they or I might have as a result of them.

People’s social graphs are exceptionally complicated. The FOAF proposal is a long way from providing even the beginnings of being able to express the relationships we have. I’ve played in a band with someone across the street, who baby sits for us on occasion. Are they my neighbour? A fellow musician? My baby sitter? Computers struggle with such vagaries, some people thrive on them. A social graph is not a simple star with me in the middle and people around the outside. It is actually a complex mixture of more and less connected individuals.

In using social networking platforms for marketing, agencies often seek out the person with the most ‘connections’ or ‘friends’. That is an error. Who is going to be more effective in propagating a message – someone with 350 contacts, or someone with 20? It depends as much on the second and third degrees of their social graph (ie out to the friend of a friend level) as on the direct contacts in the first.

One person might know 100 contacts, another might know 10. For the person who knows 100 contacts each of those people might know 100 or they might know a 1,000. Some of them may be very well connected , some may have a few tightly formed relationships, that are heavily meshed –  where all their contacts and mutual friends are related. Others may be outliers, or bridgers as I like to call them, sitting across different communities. They might only have a few relationships. but they bridge between large communities.

Social software, even in its current form, is effective in the ‘discovery’ phase of relationships. One of the reasons that Twitter is so popular with many is that it makes it easy to find new people, based on their interests or experience, and start exchanges with them. Browsers like Flock aim to integrate the social and data aspects of web surfing. The area certainly has a lot of potential, for example knowing that a web site is written by a friend of a trusted friend might have me interpret the information as more trusted than that of a total stranger (for better or for worse). There are applications that generate FOAF from your Facebook page , Firefox includes a built in FOAF browser.

FOAF also has the potential to act as a format for porting our social graphs from one social networking platform to another (as long as the platforms stop banning people for running export scripts). A number of platforms (at last a dozen at last count) already allow exporting data as FOAF information. The approach might also be useful in the Enterprise 2.0 context, where social graphs might need to be used across applications.

All of this is, of course, still in a nascent stage. Be it FOAF, or a functionally equivalent standard, we will be seeing a lot more activity around the portability and interpretation of social graph data in the coming year. In the mean time, don’t forget that it is all about connecting with people!

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In today’s hyper-connected world, where technology lets us have hundreds (if not thousands) of ‘friends’, people are increasingly interested in understanding what the human limits on maintaining human friendships might be, and why.

Real world relationships have been studied by psychologists, sociologists and host of other ‘-ists’ for decades. However, Dunbar, who works in the area of behavioural brain science, has emerged as one of the most frequently quoted figures, in the blogosphere at least.

Where did Dunbar’s Number come from?

One of Dunbar’s papers, published in 1993, wonderfully titled “The Co-evolution of Neocortex Size, Brain Size and Language in Humans” is cited for something commonly referred to as Dunbar’s number. Shock number one: There isn’t really any such thing as Dunbar’s number, in the sense that people normally refer to it.

The common mythology is that Dunbar said that people can only sustain a network of 150 contacts. Strictly speaking that is not what Dunbar’s paper said. Think of humans as brains on legs for a minute, and put your evolutionary psychology hat on. Dundar argued that, in evolutionary terms, there may be an upper group size that animals can and will live in, determined by cognitive constraints – specifically the processing capacity of the neocortex - and selected for based on various environmental constraints.

Effectively, from an individual animal’s point of view, the neocortex size sets a limit on the number of relationships that can be maintained. That in turn limits the maximum group size for the community that individual lives within, assuming they have the same constraints. At a simple level, if a species is made up of individuals that can only sustain 10 relationships, we might expect to see groups of 10 wandering around. If another member attempts to join the group, the individuals don’t have the capacity to support that extra relationship, and the member wouldn’t be accepted, or the group would fail.

The neocortex size is driven by all sorts of ecological factors that select for group size, but we could potentially use that relationship the other way round to predict group sizes, based on the neocortex. Take one group with a known group size and look at the size of its neocortex. Work out a ratio, then take another species and look at the size of its neocortex and use that ratio to predict how many individuals it would be able to support.

Would the Real Dunbar’s Number Please Step Forward

Dunbar took existing data from a number of primate studies, where typical group sizes can be observed. He then looked at the neocortex size for those primates and projected forwards to the larger human neocortex. His calculations predicted that human group sizes would typically be around 147.8. It should also be noted that Dunbar worked with average group sizes, not maximum sizes.

Now, the maths is much more complex than this summary indicates, but I’ll spare you the detail. Even so, the statisticians out there are probably gagging on their most recent meal at this point. Hang on in there. Some statistical juggling means that confidence limits around this number can be calculated, which ends up giving a range being between 100 to 231, hence my earlier comment about Dunbar’s number not really existing in the way most people expect. Think of it as a range of typical group size, rather than a number limit. Dunbar’s work has been criticised and supported. However, it is interesting to note that his suggested number does seem to match with studies of human group size from other disciplines. I commonly read both military and business books that suggest a number in this range as the typical or maximum size for a group.

Speaking of Language

There is an interesting aside here. Dunbar and others argue that social grooming is important for maintaining relationships, and for sustaining the coalitions that facilitate large group structures. While apes might spend their time picking insects out of each other’s fur, we waggle our tongues and use our voice boxes. Evolutionary psychologists often argue that we evolved language as a very efficient form of social grooming. Well, half of them would. The other half would argue something more along the lines that our brains got so big and heavy we had to do something useful with them, and language turns out to be a rather beneficial thing to have.

Think of it this way: If a large group of apes is only able to be large because they spent their entire time picking nits out of each other’s fur, it won’t be a large group for very long. While they have the advantage of being able to defend themselves and pool resources, they don’t have any time left for finding food. They will be an extinct bunch of apes in very short order.

One of the factors that gives us large group structures is our ability to use language in communication. Language is much more efficient than picking nits out of fur. I can deal with more than one person at once, for a start. That means we can be more efficient about maintaining relationships, using quick bursts of language, rather than all of the time being taken up with social grooming. Personally I find that a great relief. I love communicating with you, but I’d rather not have you dealing with my parasites!

A simpler summary would be that available time, combined with efficiency, determines the number of relationships that can be supported. Language enables us to be more efficient with our time. That in turn enables us to build a larger social world, and still have time to do other things.

Does Social Media Make Us More Social?

One of the (many) aspects of social media of that fascinates me is this: Can it enable us to be more efficient and effective in maintaining relationships? Can computer-based tools enable our brain to cope with more than it would be able to otherwise? If so, that has social ramifications, as well as organisational design ones.

So, if Dunbar’s number (or one of the equivalents from Anthropological studies) is so small, how do we end up with significantly larger groups, like 1,000+ person companies? It comes down to rings of friendships. Think of bands of 30-50, then clans of 100-200, and above that tribal groups of between 500 and 3000. Imagine that I have 30-50 relationships, and those individuals have partially overlapping relationships with others. You can now imagine an inner group and an outer group, with cohesion maintained by those individuals holding relationships across the different bands and within bands.

The way that large groups work is significantly more complex than suggested here so far. Dunbar and others argue for these layers or rings of friendships, with different strengths at each layer. This layered structure enables sustainable group dynamics. The coalitions mentioned earlier are important, since these stronger relationships provide the individual with others to protect them from potential hostility from members of the larger group, by individuals with relationships to both parties.

Bands and clans interact in a way that protects individuals and sustains tribes and population, and reflect different types and strengths of relationship. One of the challenges of today’s social media is that it doesn’t model this subtlety and complexity. Psychologists are still trying to understand the diverse nature of human relationships, and the complex properties that they have.

The Future is Still Social

It may be many years before social media catches up with even today’s understanding, and by then that understanding may have moved on – potentially due to social media itself. Social networking tools let us understand how we maintain relationships, by giving us greater visibility into how people interact, but they also potentially change the way that we do these things, a kind of social version of the schrodinger’s cat problem.

There are still going to be psychological limits on how many relationships we can maintain, whether we fundamentally change them or not. Perhaps technology enables us to have a feeling of maintaining more relationships, or it deepens relationships that have been weakened by our modern life styles. At a more human level, it is raising the question of what we mean by  ’friend‘. It is certainly making qualitative changes to what we know about those around us, and our ability to discover new people to communicate with.

Maybe you’ve worked out your own personal Dunbar number, or found ways of recreating those different circles of friendship with social media? Do you see tribes and bands in action on-line?

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Paul Graham wrote an essay “You weren’t meant to have a boss” which is really about large versus small companies, and it raises some good questions – if a little controversially.

However, it is Robin Dunbar (now teaching at Oxford) who produced the most famous research, back in 1993. His work was popularised in Malcolm Gladwell’s book ‘The Tipping Point’ – to such extent that many in the social media space talk about “Dunbar’s number“.

Dunbar took research on non-human primate social groups and used some (fairly finger in the air – by his own admission) statistical methods to extrapolate this to humans. Based on our brain’s larger neocortex size, he predicted 150 as the mean size limit for a human’s meaningful social network.

This has been widely used as a sound-bite, even featuring in The Wall Street Journal (article written by Carl Bialik). In a later paper (2003), Dunbar talks about a number range of 100-300 as the number of people in our social world (defined as the people we might turn to in severe stress, or at least approach at the airport if we needed help).

Now that we have social software, we can study people’s social graphs in ways that were very difficult previously. That said, some of the research is a little esoteric, for example Christopher Allen has an interesting post with links to research on the playon blog, looking at data from groups on World of Warcraft (for the non-gamers out there, this is a massively multiplayer on-line role playing game). We have to remember that on-line games and social networking sites like Facebook don’t directly relate to real life relationships – much as the average Facebook addict might find that hard to accept.

Researchers also have the concept of subgraphs – essentially tighter ‘cliques’ that exist within the social graph, as clusters of more tightly meshed relationships, that is individuals who have more mutual friends. In his book ‘Evolutionary Psychology’, Dunbar talks about circles of intimacy – different rings of friendship, with different levels of intimacy. We can map that to our own lives, where we usually have a smaller group of people that we are closer to.

Although Dunbar doesn’t use social networks, his view is that they might help our brains push past this limit. However, on-line networking doesn’t replace the social grooming required to maintain relationships. We still need to meet ‘IRL’ (in real life). He isn’t sold on the idea that social networks make his number outdated. Language may provide a cheaper form of social grooming – it certainly beats picking nits out of your friend’s hair – but it isn’t clear if communication technology provides even greater short cuts. The research, Dunbar says, “made us realize people don’t know what these wretched things called relationships are — and that helps explain why we’re so bad at them”.

Continued in Part II >>

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I get a handy example if I google for the excellent and insightful Fred Basset – yes, I did just use google as a verb, please don’t stone me. In the results page I am overwhelmed by information on the cartoon character, rather than the new media expert. Fred is hidden in the camouflage of a mass of other data. Security by obscurity – he’s hidden in plain sight. If I Google for Benjamin Ellis, I account for most of the first page of results – your mileage may vary searching with Google from other countries (just for fun, e-mail the first page of results from where you live!). I’m not working as an SEO consultant for myself, there just seem to be less Benjamin Ellis’s out there, so I can’t hide.

Digital information has a rather free-flowing nature. Its natural tendency is to ‘escape’ from where we put it. Unhappy accidents like the recent HMRC fiasco are a reminder that it has a characteristic that physical property does not: it can be replicated, indefinitely.

If I mark something as ‘private’, to share with my ‘closed’ social network, I am reliant on those friends not making it public – either purposefully or accidentally. For example, if they tweet it on twitter, then it is indexed in Google by default. In the same way, companies rely on employees keeping information confidential. The difference is that data spillage now happens more easily, with our increasing connectedness.

There is an interesting characteristic of digital conversations that take place in social media, and that is a form of digital ‘spill’. The characteristic springs from the mismatch between peoples’ social graphs – your set of friends/contacts and mine may have some common elements, but they also have differences.

If we ‘chat’ between ourselves via the Facebook (using the wall feature) or Twitter, the differences in our social graphs cause shards of the conversation to propagate our beyond the original circle. That can be bad, or it can be good. One of the most interesting things about Twitter is the accidental conversations. It is the closest thing to creating that business haven of innovation, the water cooler conversation. With more and more remote workers, and reliance on external specialists, business will need these tools.

The reality is that Facebook is just describing the real world of social relationships. There is nothing new here. ‘Social graphs’ have existed since humans first started raising children and gathering food together. Now we have a common place word to describe the phenomenon, and tools, like Facebook and Linkedin, that have digitised the information and enabled us to study it as never before.

Data has the rather nasty habit of being permanent, sometimes inconveniently. I recently stumbled upon an email I sent to a mailing list in 1988, which is now a web forum. There is my email, in all of its glory. Thankfully I wasn’t too embarrassing as a teenager, but none-the-less, it is quite a sobering fact that something I wrote twenty years ago is right there, neatly indexed on Google.

The real world of information security, especially around identity, is messy. Tools like Facebook are gradually drawing attention to old issues and creating new ones. In the first few decades of computing, the challenges were in the technology, in the next, I suspect the challenges reside elsewhere.

Having a universal digital identity has efficiency benefits, but it also has big data privacy challenges too. It takes discoverability to a new level, which means that integrity is going to take on a whole new meaning, however good your security is.

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As it turns out, he was the one and only Boris Johnson. As we chatted, walking together towards city hall, I have to say I was impressed. If I had a vote, I might even vote Boris for London Mayor. It must be draining having people saying, “Are you who I think you are?” all day, sorry Boris!

We are all increasingly public figures these days, with the proliferation of social networking (Facebook, LinkedIn, …), messaging (like twitter), and blogging. All of these systems create new challenges, as aspects of our identity are increasingly digitised and stored.

We aren’t that clean and distinct individuals. I’m not talking about our personal hygiene, but about us as social objects. In the words of Paul Simon, “one man’s ceiling is another man’s floor.” In the case of digital identities, one man’s sealing is another man’s flaw:

Take Robert Scoble and Facebook. Scoble used a script from Plaxo to read information from Facebook, syncing his (very large) social graph from one to the another. However, Facebook like to seal data into their systems. For Scoble this was a flaw. You can read more in the article “Facebook bans Scoble…” by the ever present Mike Butcher of Tech Crunch UK. There is also more detail on the Plaxo script in wired.: “Scoble’s Slap…” .

One of the issued this stirred up is exactly who’s data is it on Facebook anyway? And what rights do different people have over it?

• Is it yours? It is your social graph after all, which is part of your digital identity.
• Is it Facebook’s? They created the platform that crystallised and stored the data – see the Facebook terms and conditions.
• Does it belong to your friends? You are part of their social graph and identity too.

The philosophical answer is probably a resounding ‘yes’ to all three, but that doesn’t help in the real world. The portability of social data is going to be a serious issue for a while, both for businesses and for users. Social tools have some great productivity benefits, but they are raising many new challenges.

There is a reasonable desire to easily move data from one silo to another. This is being championed by the open data movement. They are driving ways to make identity data more portable, but this forces the question of data ownership. If you want to follow more on this, I recommend Jeremy Keith’s blog, starting here. The standards and technology exist to do this (APML, OPML, RDF, microformats, openID, …). It is a question of them being used. Why have a different login and profile for every supplier and system that I use? Why have to re-import all of my contacts and connections. The business world faces the same challenges of cross business identity, and also stands to benefit – one person’s CRM is another’s VRM as they say.

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