Influential people + influential friends = spread products

Identifying social influence in networks is critical to understanding how behaviors spread. We present a method for identifying influence and susceptibility in networks that avoids biases in traditional estimates of social contagion by leveraging in vivo randomized experimentation. Estimation in a representative sample of 1.3 million Facebook users showed that younger users are more susceptible than older users, men are more influential than women, women influence men more than they influence other women, and married individuals are the least susceptible to influence in the decision to adopt the product we studied. Analysis of influence and susceptibility together with network structure reveals that influential individuals are less susceptible to influence than non-influential individuals and that they cluster in the network, which suggests that influential people with influential friends help spread this product [red text highlighting added].

Identifying Influential and Susceptible Members of Social Networks
Sinan Aral, Dylan Walker


Social media have provided plentiful evidence of their capacity for information diffusion. Fads and rumors but also social unrest and riots travel fast and affect large fractions of the population participating in online social networks (OSNs). This has spurred much research regarding the mechanisms that underlie social contagion, and also who (if any) can unleash system-wide information dissemination. Access to real data, both regarding topology—the network of friendships—and dynamics—the actual way in which OSNs users interact, is crucial to decipher how the former facilitates the latter’s success, understood as efficiency in information spreading. With the quantitative analysis that stems from complex network theory, we discuss who (and why) has privileged spreading capabilities when it comes to information diffusion. This is done considering the evolution of an episode of political protest which took place in Spain, spanning one month in 2011

Locating privileged spreaders on an online social network

Javier Borge-Holthoefer, Alejandro Rivero, and Yamir Moreno

Phys. Rev. E 85, 066123 (2012)

Who believes in the 90-9-1 rule?

A second question on LinkedIn from Dr Michael Wu, Principal Scientist at Lithium Technologies:

Is there something more accurate and precise than the 90-9-1 rule out there? IMHO, Lorenz Curve and Gini Coefficient. Do you know anything else? The Economics of 90-9-1

My answer as part of yesterday’s Online Community Manager group discussion kind of sums up where I’ve got to after reading Dr Wu’s blog previous post and this latest one:

I like the approach you have using economics-based models. I’ve come at it from a more particpant-observer type sociological point of view, so what I’d like to see is for your analysis to return a new ‘rule of thumb’ based on your in-depth data analysis.

The 90-9-1 rule is useful to community managers because it helps provides a starting point for understanding, as Arantza says above. For example it would be useful to know from a practical point of view whether for more open communities (as opposed to niche market research or project based communities) the 90-9-1 is a useful tool for helping launch a new community.

It’s partly about creating a social dashboard that can explain to a member of senior management why a certain kind of community activity may help or hinder greater participation.

I did this kind of work previously in the National Health Service, creating simple reports on the success of a national public health initiative, which worked well for senior managers (government ministers in that case).

So I come back to the challenge, the age old relationship between lab & fieldwork if you like, what would be the new rule of thumb/thumbs?

I’ve chosen to highlight multiple feedback loops as a useful tool, to help drive top contributors for example (taken from the HP Labs research), but I take your point that for commercial ROI purposes more precision is required. To put it another way in such a dynamic social context how does precision allow you to create heuristics for day to day community management?