The study, with the snappy title “A Scalable Heuristic for Viral Marketing Under the Tipping Model,” examined 36 social networks (including nine academic collaboration networks, three e-mail networks, and 24 networks extracted from social media sites) to deduce an approach for identifying “seed groups” -- the subsets of larger groups who are most likely to propagate a viral marketing message.
The model is based on the number and structure of social connections within that subset and the larger community; in essence, it assumes that an individual will “hear” a message once it has been repeated by a certain number of their acquaintances, reaching a critical threshold. That individual is then converted into an additional propagating point for the message.
As part of their approach the researchers analyzed each network to determine how susceptible it is to viral sharing of content, and found that general-purpose online social networks were the most susceptible (meaning, requiring the smallest seed groups to propagate content virally). In many cases they were also able to identify a takeoff moment, or moment of “critical mass,” when the viral spread of content suddenly accelerates.
Promisingly the method for identifying “seed groups” worked even when the most central, highly-connected “nodes” (individuals) were removed -- which in real-world terms means you don't necessarily have to recruit, say, a celebrity to kickstart your viral content strategy.
The study also determined that in many cases the “seed group” may be quite small in size, but still an effective starting point for viral spread of content, ultimately “tipping” a much larger network. Less promising, the study found that dense, tightly connected “local neighborhoods” actually inhibit the spread of viral content on social networks (I picture this as rocks in a pond disrupting a ripple effect).