April 24th, 2009

Behavior Adoption on Social Networks

Why and how do people adopt new behaviors? Why do they start using new products? Did you sign up for Facebook because all of your friends were on it, or because a specific friend recommended it to you? Or do you refuse to sign up at all?

In this article I’m going to outline two models that describe how new behaviors, ideas, and messages propagate through social networks.

The Threshold Model

The first model is called the Threshold Model.1 It says that people adopt a new behavior bceause a sufficiently large proportion of their friends have adopted that behavior. Early adopters have a very low threshold, say 5% or 10%, while late adopters would have a much higher threshold. Every person, however, has their own individual threshold.

For example, my girlfriend’s stated reason for signing up for Twitter was that “all my friends were using it.” And during the 2008 US Presidential election, some Obama supporters would adopt Hussein as their middle name.2 When I saw that lots of my friends were doing it I was certainly tempted to do the same.

The underlying psychological principle is one of “missing out” or “when in Rome.” The key variable here is the initial distribution of thresholds across a social network, which describes in totality the final extent of the behavior.

It’s worth noting that this model says nothing about how people initially adopt behavior. That is, it says nothing about innovators, only about the spread of innovation through a social network.

The Cascade Model

The second model is called the Cascade or Word-of-Mouth Model3, and is the method of “viral growth” that most social application developers are familiar with. It says that every person has a chance of adopting a new behavior whenever one of their neighbors adopts it.

This model describes phenomena like product recommendations or user-to-user notifications on Facebook. The probability that a person adopts the new behavior is the conversion rate for the notification.4

This probability is both a function of the sender and the recipient, so more influential people are more likely to convince you to adopt a behavior (or purchase a product, or install an application).

Practical Implications

Both of these models describe facets of real-world interaction on social networks. My take is that the cascade model is more accurate at the beginning of a social network’s life, where behavior is spreading through sparse areas, connected by influencers. Later on, after a critical density has settled in, people start adopting the behavior because everyone else is adopting it and there’s a social cost to not doing the same.

We see this pattern in services like Facebook and MySpace, both of which got their start by harvesting emails and spreading through word-of-mouth (and spam) across a social network.5 Eventually each network reached a point where a sufficient number of people were familiar with the product and new users adopted it not because their friends recommended it (the cascade model), but because there was a social expectation that they do (the threshold model).

Also, with respect to analytics and viral growth, the threshold model is more difficult to track. In the cascade model we record who sent what to whom and which messages they responded to. It’s clear who gets credit for a user’s conversion. In the threshold model you have to track passive exposures, and there’s no clear causal relationship.

If ten of my friends are doing something and I decide to start doing the same thing, who gets credit? Most analytics packages will show this behavior as a direct visit, with no connection to other users’ behavior, even though there is a viral process underlying it.

In short, the threshold model requires a certain level of behavioral density, while the cascade model doesn’t. However, we see both models expressed in how people actually adopt new behaviors in social contexts.

Formalisms

In the threshold model every person u has a threshold

T_u \in [0,1]

and each of their neighbors v is weighted according to

w_{u,v}

If

then the person u adopts the behavior.

The set of thresholds, weights, and initial adopters completely determines the extent of the behavior in the social network.

In the cascade model, for every person u and neighbor v there is a random variable

X_{u,v}

which describes the likelihood of u adopting the behavior if v has adopted it.

Takeaways

I’ll try to boil all this down into a few, practical takeaways.

  1. The Threshold and Cascade Models describe two mechanisms of behavior adoption in social networks.
  2. The Threshold Model says that people do something if enough of their friends are doing it.
  3. The Cascade Model says that people have a chance of doing something if one of their friends is doing it.
  4. Both models correspond to different real-life adoption patterns.
  5. The typical “viral loop” involves the cascade model, but most successful social networks rely on the mechanics of the threshold model in the long run, i.e., density is important for long-term success.
  6. The cascade model is a good tool for analyzing acquisition scenarios, but the threshold model is probably more helpful for understanding retention and engagement — it at least implies that density is a key factor in social network growth, a metric that’s not often discussed publicly.

Agree? Disagree? Leave a comment, send me an email, or follow me on Twitter!

  1. See Threshold Models of Collective Behavior (1978) by the famous sociologist Mark Granovetter. []
  2. See Obama Supporters Adopting Middle Name “Hussein” As Their Own []
  3. See Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth (2001) by Goldenburg, Libari, and Muller. []
  4. More accurately, we’d model the “probability” as a random variable whose mean was the conversion rate. []
  5. See Stealing MySpace: The Battle to Control the Most Popular Website in America for details about the MySpace team’s background in direct marketing. The ConnectU vs. Facebook court documents, which you can find via Google, paint a similar story for Facebook’s early years. []
  • DarcyKitchin
    I never thought of actually using mathematics to describe the Facebook/Twitter phenomena but I do appreciate you doing it. I joined these social networks mostly because I needed to gather some contacts. I managed to find a lot of my high school friends and they're scattered all over the world. At this point, Twitter isn't helping me much but who knows. Anyway, I started commenting on photos and adding "likes" to all sort of things mainly because other friends were doing this to my pictures and links. So I am acting like my peers.
    Darcy Kitchin @ Windows virtual server
  • GlennLEU
    Question. What kind of viral growth model does http://www.dirtyphonebook.com fit into? The "insult all of your users just for kicks" model?
  • Very confusing post. Not sure I quite soaked in all that info. Can someone elaborate?

    google bizkit
  • Without going to physical statistical models (though they're a good place to go), I see both the threshold and cascade as variants of the same underlying mechanism as well.

    The random factor in the cascade model exists as part of the model only because we do not know the weight of an individual relationship in terms of its degree of influence. Put another way, if the weight in threshold model is described as a probability [0,1] that an individual will follow the influencing person's lead, then we can aggregate all such influences (not necessarily by simple addition) to describe the threshold and cascade function.

    To make that more specific, if three guys at work extol a particular idea, they will each have a variable effect (based on your trust in their opinion -- it's not random) on whether you start thinking positively about this idea as well. The aggregation matters: if you think highly of two of them and detest the third, the probability is less than if you trusted the opinions of all three. If however another far more influential person (a well-known blogger or celebrity) also extolled that position, that influence would have a disproportionate effect on your opinion commensurate with your trust in that person's opinion.

    The mechanism bears a significant similarity to dendritic summation in neurons and probably other similar phenomena; it's not restricted to social contexts. Variable weight inputs aggregate in terms of their influence or effect, encompassing both what you have called cascade and threshold.

    Oh, it's also worth noting that (as in neurons :) ) the *absence* of input can be important too -- this is likely part of what drives early adopters. For them, getting high-influence input from a few trusted individuals isn't sufficient to trip the threshold; there must also be a lack of ambient input. That is, if everyone else is already doing it, the early adopter loses interest. The variance in reliance on a few high-trust inputs without ambient input or many more ambient low-trust inputs describes the spectrum from early adopter to laggard in any population.
  • Interesting. I'm working on a viral growth visualization project and was describing it to people as a sea of neurons. Diffusion of information or behaviors in the social network follows a lot of the same principles, as I understand it, e.g., all-or-nothing.

    Do you have any information about actual formal models of these phenomena? The more math the better!

    And thanks so much for the comment — really awesome.
  • I do have some formal models for this, but I'm not aware of any that have been published -- if you run across any I'd be interested to hear about it. The models I have are what we use to drive our "social context" software, and so we don't really give them out. :)

    In both social and neural cases, there are interesting blends of all-or-nothing and smooth ramps of transmission. Any given neuron fires or doesn't, but can fire with different intensity -- and it can have a variable excitatory or inhibitory effect on other neurons. Similarly, someone can pass an opinion to someone else (or not), and then there are several other factors that come into play: how strong is the opinion, and how much does the receiver believe it. Then there are things like personality factors that moderate whether the receiver acts on the information and/or tells others about it, which starts the cycle over again.

    At a simple level, you need to have people tell more than one person about a cool new idea or product to give it virality. How much more than 1 depends on the other probabilities -- but higher values increase spread quickly.
  • jasonwatkinspdx
    Good article.

    One feature you may be missing is preferential attachment. Celebrities like scooble and oprah have drawn in a huge volume of twitter users. Myspace had similar dynamics due to bands in it's early days. The growth of graphs under preferential attachment is formalized and it's characteristics mostly well known:

    http://en.wikipedia.org/wiki/Barabási-Albert_model
  • I agree to Jason
  • Indeed, and thanks for taking the time to read and comment!

    Celebrities and the like are an interesting phenomenon in social networks. They are always highly connected, moreso on directional SNs like Twitter and MySpace, but the extent to which they are influenced or cause influence are much more ambiguous. Their effect on behavioral and opinion dynamics is probably a research topic all in itself.

    And I actually wrote a Ruby script to generate BA graphs just the other day!
  • Mike
    Is it possible that these are the same model? The transition from one model to the other sounds a lot like the phase transition studied by statistical physicists (e.g., Ising models).
  • Yes, both "models" are simple forms of an Ising system (paramagnetic and ferromagnetic). The Threshold is the coupling to an external field and Wuj are the couplings between nearest neighbors along the edges of the network. Research in similar models (mostly in the context of magnetism) has been on-going since the 20s (see http://en.wikipedia.org/wiki/Ising_Model )
  • Interesting. I don't know anything about statistical mechanics. Can you elaborate?
  • You explicitly said that this doesn't include innovators, but I think that's an important thing to consider.
    The very earliest adopters aren't trying a new thing because they have some percentage of friends using it; rather, the motivation is that they DON'T know anyone using it yet. They want to be able to be part of that first 1%, to be the originator of the trend.

    Why is this important? Because, as the provider of the social network, it's in your interest to build in a way to recognize these innovator/pioneers. Offering something equivalent to "a low ICQ number" or marking the user's avatar can drive these initial innovators, which, in turn, creates the 5% necessary to start the Threshold condition.
  • Cindy,

    Good point.

    The way I see most social networks (and social apps) growing is by bootstrapping through the cascade model, until they're dense enough in their parent network that they become self-sustaining and threshold psychology becomes the dominant factor.

    The risk with being too aggressive with invites early on is that you never build up the density to become self-sustaining. By promoting early adopters and keeping them engaged, you can help foster the core density from which your social network can grow organically.

    FWIW, although Facebook bootstrapped by spamming everyone in Harvard, they grew with the threshold model in mind from day one. As I understand it they never even considered signing up a new campus until there were at least 40 people from that campus requesting accounts. And they started with smaller campuses where density as easier to create and sustain.
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