Imagined Audience and Micro-celebrity [...]

This article investigates how content producers navigate ‘imagined audiences’ on Twitter. We talked with participants who have different types of followings to understand their techniques, including targeting different audiences, concealing subjects, and maintaining authenticity. Some techniques of audience management resemble the practices of ‘micro-celebrity’ and personal branding, both strategic self-commodification. Our model of the networked audience assumes a many-to-many communication through which individuals conceptualize an imagined audience evoked through their tweets. (Source)

Complex Contagions [...]

Homophily increases local spread at expense of global spread. Viral phenomena must spread locally and globally.

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications. (Source)


See also Sanders Filter Bubble, Streams Don’t Merge

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Degree Assortativity [...]

In human networks, a principle called " degree assortativity" is the norm. It turns out to be resistant to viral effects, and that may have evolutionary roots in survival.

Let me give you an example. This is very visual and, given this format, I'm not supposed to use visuals, but I'm going to cheat and use one slide in a moment. Let's say you had 1,000 people, and, on average, they each have five connections, so you have 5,000 ties between them. Mathematically, you could construct a number of ways in which you could organize these networks. You could have a random network where people are jumbled together; you could have a big ring network; you could have a kind of “scale-free” network; you could have the kind of network that we humans actually make (which has a variety of properties). It turns out that if you were designing the network from mathematical principles so that the network would be the most resistant to pathogens taking root within it; so, you say, "I want to organize these people in such a fashion that this group, when so organized, resists epidemics;" whereas, if they'd been organized some other way, these same people who otherwise were identical—had the same immune systems, the same biology—this group no longer resisted epidemics so well. If you wanted to give the group the epidemic resistance property, the way you would organize the people is to give them a property in network science known as degree assortativity. You would make popular people befriend popular people and unpopular people befriend unpopular people. You could give them this property, it would make the network as a whole resistant to germs being able to make inroads.

And I can cultivate this intuition by asking you to think about the airport network in this country. The airport network is degree disassortative. Chicago is connected to lots of small airports but, in the small airports, you can't fly from one to the other; they are disconnected from each other. Whereas people don't have that property. Popular people befriend popular people, and unpopular befriend unpopular. Now, think about which of those two networks, if you were a bioterrorist and you wanted to seed a germ in, which network would the germ spread more rapidly? In the airport network, right? If you start any random node, like an isolated small town, it will go to Chicago, and, in the next hop, it will reach the whole nation. But if you had the hubs and the spokes or the peripheral airports connected to each other, it would be relatively more impervious to a pathogen spreading.

I don't think it's a coincidence that of all the kinds of ways human beings could organize themselves into networks, that's what we do. We evince degree assortativity, and I don't think it's a coincidence that we do that. We assemble ourselves into groups, the group now has this property, this germ- resistance property, which is a property of the group, but which, as it turns out, also benefits and affects us. Now, being a member of that group, we are less likely to acquire pathogens. (Source)


In order to bypass Degree Assortativity, contagions must be Complex Contagions

The tendency for things to be simple contagions may be a problem. See Sanders Filter Bubble

Wikipedia::Assortativity

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