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Influence et contagion
Trends sur l'influence et la contagion dans la cyberculture
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Evolution of Online User Behavior During a Social Upheaval | #datascience #diregeziparki

Evolution of Online User Behavior During a Social Upheaval | #datascience #diregeziparki | Influence et contagion | Scoop.it
luiy's insight:

Social media represent powerful tools of mass communication and information diffusion. They played a pivotal role during recent social uprisings and political mobilizations across the world. Here we present a study of the Gezi Park movement in Turkey through the lens of Twitter. We analyze over 2.3 million tweets produced during the 25 days of protest occurred between May and June 2013. We first characterize the spatio-temporal nature of the conversation about the Gezi Park demonstrations, showing that similarity in trends of discussion mirrors geographic cues. We then describe the characteristics of the users involved in this conversation and what roles they played. We study how roles and individual influence evolved during the period of the upheaval. This analysis reveals that the conversation becomes more democratic as events unfold, with a redistribution of influence over time in the user population. We conclude by observing how the online and offline worlds are tightly intertwined, showing that exogenous events, such as political speeches or police actions, affect social media conversations and trigger changes in individual behavior.

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GLEAMviz.org – The GLEAM Simulator system | #dataviz #complexity #prediction

GLEAMviz.org – The GLEAM Simulator system  | #dataviz #complexity #prediction | Influence et contagion | Scoop.it
luiy's insight:

The GLEAM Simulator system consists of the GLEAM Server and the GLEAMviz Client application.

 

The GLEAM Server uses GLEAM as the engine to perform the simulations. This server runs on high-performance computers managed by the GLEAM project.

 

The GLEAMviz Client is a desktop application through which users interact with the GLEAM Server. It provides a simple, intuitive and visual way to set up simulations, develop disease models, and evaluate simulation results using a variety of maps, charts and data analysis tools.

 

 

 Visualisation and analysis

 

GLEAMviz offers three types of visualization. The first shows the spread of the infection on a zoomable 2D map while charts show the number of new cases at various levels of detail.

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Study suggests that social-network support for causes might be only a click deep | #SNA #donations

Study suggests that social-network support for causes might be only a click deep | #SNA #donations | Influence et contagion | Scoop.it

Social media may fuel unprecedented civic engagement.


Via Jesse Soininen
luiy's insight:

Lewis and colleagues analyzed the donation and recruitment activity of more than 1 million members of the Save Darfur Cause between May 2007 and January 2010. About 80 percent of the members had been recruited by other members and about 20 percent had joined independently.

 

Of these 1 million-plus members, 99.76 percent never donated any money and 72.19 percent never recruited anyone else.

 

The Save Darfur Cause on Facebook raised only about $100,000. While the average donation amounts were similar to more traditional fundraising methods ($29.06), the donation rate was much smaller: 0.24 percent. Compare that to mail solicitations which typically yield donation rates of 2 to 8 percent. The larger Save Darfur campaign, the researchers note, raised more than $1 million through direct-mail contributions in fiscal year 2008 alone.

 

Interestingly, those that had joined the Facebook cause independently were both more likely to donate and to recruit.

Social and financial contributions, though rare on both counts, also tended to go hand-in-hand. Those individuals that did recruit were nearly four times as likely as non-recruiters to donate. And donors were more than twice as likely as non-donors to recruit.

The data contained no demographic information on the cause's members. Nor, the researchers write, could they estimate "the personal significance of [the joining] gesture to participants or the symbolic impact of the movement to onlookers.

 

"It is possible," they add, "that the individuals in our data set contributed to Save Darfur in other meaningful but unobserved ways."

 

Still, Lewis and colleagues believe the study gives some valuable insights into collective action in a digital age.



Read more at: http://phys.org/news/2014-03-social-network-click-deep.html#jCp

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#contagion : Social Contagion and Cascade Behaviors on Twitter

It has been found in a variety of face-to-face networks that diffusion of information, behaviors and sentiments extend up to two to four degrees of distance from the original source.
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How to Find the Best Connected Individual in Your Social Network | #SNA #influence

How to Find the Best Connected Individual in Your Social Network | #SNA #influence | Influence et contagion | Scoop.it
Field experiments in rural India have revealed a cheap and simple way to find the best connected individuals in any social network–just ask the people.
luiy's insight:

Banerjee and co made their discovery by studying the network of links between individuals in 75 rural villages in southwest India. They measured these networks by asking people who they visited, who visited them, who they were related to, who they borrowed money from, who they lent money to, and so on.

 

They then asked people in 35 villages the following question: “If we want to spread information about a new loan product to everyone in your village, to whom do you suggest we speak?”

 

The results provide a fascinating insight into the knowledge humans build up about their social networks. When people answered this question (and substantial numbers didn’t), they unerringly identified central individuals within their village.

 

 

 

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News Information Flow Tracking, Yay! (NIFTY) : System for large scale real-time tracking of #memes | #datascience #algorithms

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The Structure of Online Diffusion Networks I #adoptions #patterns

luiy's insight:

In order to identify generic features of online diffusion structure, we study seven diverse examples comprising millions of individual adopters. As opposed to biological contagion, our domain of interest comprises the diffusion of adoptions, where “adop- tion” implies a deliberate action on the part of the adopting individual. In particular, we do not consider mere exposure to an idea or product to constitute adoption. Conta- gious processes such as email viruses, which benefit from accidental or unintentional transmission are therefore excluded from consideration.


Although restricted in this manner, the range of applications that we consider is broad. The seven studies described below draw on different sources of data, were recorded using different technical mechanisms over different timescales, and varied widely in terms of the costliness of an adoption. This variety is important to our con- clusions, as while each individual study no doubt suffers from systematic biases arising from the particular choice of data and methods, collectively they are unlikely to all ex- hibit the same systematic biases. To the extent that we observe consistent patterns across all examples, we expect that our findings should be broadly applicable to other examples of online—and possibly offline—diffusion as well.


The remainder of this paper proceeds as follows. After reviewing the diffusion liter- ature in Section 2, in Section 3 we describe in detail the seven domains we investigate. We present our main results in Section 4, showing that not only are most cascades small and shallow, but also that most adoptions lie in such cascades. In particular, it is rare for adoptions to result from chains of referrals. Finally, in Section 5 we discuss the implications of these results for diffusion models, as well as the apparent discord between our results and the prevalence of popular products, such as Facebook and Gmail, whose success is often attributed to viral propagation. 

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