Losing the Forest in the Digital Trees [...]

New study suggests that we are losing the forest for the trees when we read digitally.

There are several explanations for why mobile digital technologies may prime or trigger a lower-level, concrete mindset in individuals. As noted earlier, prior work has shown that even brief experiences with digital technology for newcomers can have significant effects on neural networks associated with working memory and rapid decision making. Likewise, a growing number of accounts attest to particular information processing habits, such as quick scanning and skimming [4, 24], and expectations, such as immediate gratification, that individuals come to associate with their interactions with digital platforms [18]. The ever-increasing demands of multitasking, divided attention, and information overload that individuals encounter in their use of digital technologies may cause them to “retreat” to the less cognitively demanding lower end of the concrete-abstract continuum. The present work suggests that this tendency may be so well-ingrained that it generalizes to contexts in which those resource demands are not immediately present.

These results are not intended to be an indictment of digital technology and its impact on cognition. Indeed, there is great value in utilizing lower-level, concrete construals of information, particularly in domains requiring the careful consideration of lower-level details, such as analytical problem solving [6] and risk assessment [11]. At the same time, if the increasing accessibility and ubiquity of digital technologies is causing a shift toward the prioritization of concrete construals of information, it is important to consider the ramifications of this trend. Thus, the present work may provide an impetus for HCI designers and researchers to consider strategies for encouraging users to see the “forest” as well as the “trees” when interacting with digital platforms.

Imagined Audience on Social Network Sites [...]

The findings reveal that even though users often interacted with large diverse audiences as they posted, they coped by envisioning either very broad abstract imagined audiences or more targeted specific imagined audiences composed of personal ties, professional ties, communal ties, and/or phantasmal ties. When people had target imagined audiences in mind, they were most often homogeneous and composed of people’s friends and family. (Source)

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

Wikity users can copy this article to their own site for editing, annotation, or safekeeping. If you like this article, please help us out by copying and hosting it.

Destination site (your site)
Posted on Leave a comment on Complex Contagions

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