Things don’t go viral (except for viruses)

You have heard the stories about viral videos that receive millions of views. One part of those stories, however, is not true. Things don’t go viral the way that viruses that do.

.…………………………..Not the way it happens

Academics, business people, and the man on the street commonly believe that interesting information will spread like a virus (I tell two friends, who tell two friends, who tell two friends, etc.). However, research by Sharad Goel, Duncan Watts, and Dan Goldstein demonstrates that belief is largely a myth. By analyzing millions of data point from seven online data sets (e.g., Twitter, Yahoo! Voice), the researchers find that most common event is that no one responds to an act. If information does get passed on to others, it typically only has one degree of separation. Check out the most common patterns (taken from this post):

  Not viral

Sharad outlines the takeaway in his blog post.

In all the examples we study, diffusion seems remarkably un-viral, rarely spreading far from an independent adopter. Our results thus call into question the dominant, epidemic-like models of diffusion, and also the value of viral marketing campaigns. On a positive note, this observation makes life a lot easier. Instead of needing to describe, predict, or trigger a complicated viral process, one can focus on the much easier case of adoptions that spread at most one hop before terminating.

 

Well then, what goes viral?

Viruses (including email viruses) do.

What is really happening?

Things get popular. A “big mouth” like the media or Justin Bieber talks about it, and a lot of people learn about it.

And what should marketers do?

Take a public relations approach. Be interesting to ‘big mouths.’

Note: there is one potential caveat to this post. The non-viral effect hasn’t been demonstrated with YouTube videos, but I would bet money that it holds.

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Goel, Sharad, Duncan J. Watts, & Daniel G. Goldstein (2012). The structure of online diffusion networks. Proceedings of the 13th ACM Conference on Electronic Commerce (EC’12). Find the paper here.

 Models of networked diffusion that are motivated by analogy with the spread of infectious disease have been applied to a wide range of social and economic adoption processes, including those related to new products, ideas, norms and behaviors. However, it is unknown how accurately these models account for the empirical structure of diffusion over networks. Here we describe the diffusion patterns arising from seven online domains, ranging from communications platforms to networked games to microblogging services, each involving distinct types of content and modes of sharing. We find strikingly similar patterns across all domains. In particular, the vast majority of cascades are small, and are described by a handful of simple tree structures that terminate within one degree of an initial adopting “seed.” In addition we find that structures other than these account for only a tiny fraction of total adoptions; that is, adoptions resulting from chains of referrals are extremely rare. Finally, even for the largest cascades that we observe, we find that the bulk of adoptions often takes place within one degree of a few dominant individuals. Together, these observations suggest new directions for modeling of online adoption processes.

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Two popular videos that did not go ‘viral’:


 

One response to “Things don’t go viral (except for viruses)”

  1. Carson Ward

    Hi Peter,

    Thanks for making me aware of this. I’d have to disagree with you that “viral” is an inappropriate term for content that gets shared widely.

    Firstly, some viruses do tend to spread on a 1:1 basis – namely any virus that is blood-borne.

    Second, no one is implying that content goes viral exactly like a virus. What we mean when we talk about content going “viral” is that the content spreads from person without the originators or marketers efforts.

    Your criticism related to targeting large nodes is spot-on, though. We hear a lot of blog posts and social media gurus suggesting that we “target influencers” and “leverage contacts with a larger share of voice.” The data shows that this advice is often bad advice, and that we need to encourage sharing from everyone who reads/views/listens.

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