Without going into links to specific posts, I’ve noticed a trend among many blogs I try to keep up with over the past couple of years. I can’t count the number of times I’ve seen prominent bloggers post publicly about having to pare down the list of RSS feeds they read, or tweets they respond to. Since Peter Kim’s blog is the most recent instance of the trend I’ll use one of his recent posts as an example of what I mean. Peter noted that he increasingly hears an echo chamber across social media blogs in which the same content, case studies, anecdotes, etc. gets repeatedly posted and commented on. More cynical observers might contend that the complaints about information overload from influentials is a little like strutting in front of a crowd. Nevertheless, it is difficult to dispute the point that attention is a scarce resource on the Web. So is engagement.
Ross Mayfield recently pointed to a study published by researchers at the Social Computing Lab of HP Laboratories that addresses the point succinctly by pointing to constraints on friendship in directed social networks such as Twitter. A directed social network is characterized by an absence of explicit reciprocity constraints, fifty people can follow one person without that person necessarily following any of them. First Monday’s most recent issue includes an article, Social Networks that Matter: Twitter under a Microscope, that reports on a study of Twitter users by Bernardo A. Huberman, Daniel M. Romero, and Fang Wu of HP Laboratories.
The authors analyzed data from 309,740 people using Twitter. They compared the network of interactions people actually engage in while using social computing technologies such as Twitter to the network of connections with whom one shares a social relationship. Networks of actual interaction are considered networks that matter by the authors.
By networks that matter we mean those networks that are made out of the pattern of interactions that people have with their friends or acquaintances, rather than constructed from a list of all the contacts they may decide to declare.
In other words, the research focused on reciprocity as well as connection in studying the social network of Twitter.
Hidden Friends, Influentials, and Dunbar Numbers
JP Rangaswami over at Confused of Calcutta offered the observation that the HP study’s findings probably ought not surprise us. The researchers distinguished between three types of people using Twitter: followers, followees, and friends. Anyone who uses Twitter is familiar with the first two categories. We choose to follow the tweets of some people on Twitter and others (not necessarily the people we choose to follow) follow us. However, the research also looks at tweets where hidden friends send @messages to specific people. Hidden friends are defined as people who received at least two @messages from others. The authors report that “90 percent of a user’s friends reciprocate attention by being friends of the user as well.”
Rangaswami contends the HP study is significant because the data presented in the research seems to call into question the assumption that social software works to raise Dunbar’s number. To quote from Wikipedia,
Dunbar’s number is a theoretical cognitive limit to the number of people with whom one can maintain stable social relationships. These are relationships in which an individual knows who each person is, and how each person relates to every other person. Proponents assert that numbers larger than this generally require more restricted rules, laws, and enforced norms to maintain a stable, cohesive group. No precise value has been proposed for Dunbar’s number, but a commonly cited approximation is 150.
The point is an important consideration for anyone thinking about the use of social media in word of mouth, or viral, marketing. Rangaswami points out “that as the number of friends increases, there is apparently no loss in reciprocity.” Indeed, the HP authors note that “reciprocity of attention is a very consistent trend as it holds for both users with many friends as well as for users with very few friends,” and plays a crucial role in defining the ‘hidden network’.”
However, Rangaswami adds a crucial point. He observes that the data from the HP study (Figure 4 — see below) indicate that Dunbar’s number, or some variation of that number, constrains the scale of networks that matter. And Rangaswami proceeds to note, “there is a suggestion that the number of friends is constrained in Dunbar-like manner.”
Rangaswami continues to think that social software can shift the Dunbar-like constraints currently evident in online networks. Ross Mayfield, on the other hand, thinks that social software helps people manage their Dunbar number more effectively.
What Social Software can do for your Dunbar number is help what goes in and out of the 150. Help you discover people you should add to your network. Stay in touch with old friends so they can be active friends for at least a moment in time. Help others sift out of view.
Indeed, Peter Kim’s solution to the echo chamber he sensed in his RSS feeds involved dropping connections and adding some new ones.
Twitter and Word of Mouth Marketing
The authors of the HP study contend that one of the most important points to take away from the research relates to the pattern of interactions in the social networks supported by Twitter.
Many people… see online social networks as an opportunity to study the propagation of ideas, the formation of social bonds and viral marketing, among others. This view should be tempered by our findings that a link between any two people does not necessarily imply an interaction between them. As we showed in the case of Twitter, most of the links declared within Twitter were meaningless from an interaction point of view. Thus the need to find the hidden social network; the one that matters when trying to rely on word of mouth to spread an idea, a belief, or a trend.
My take on the findings from the HP study is as follows: the influence of influentials on word of mouth, at least in Twitter, increases as members of their hidden network reciprocate their own followers through interaction, rather than increasing due to the total number of people who follow any particular influential. Placing an emphasis on the latter rather than the former increases the echo some hear in social media.
To me, one of the key issues from these findings is whether they speak to the big seed/little seed issue in relation to viral marketing. Anyone reading this post care to share their opinion?
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Posted by Larry R. Irons