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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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#Stigmergic dimensions of Online Creative Interaction | #algorithms #memes

#Stigmergic dimensions of Online Creative Interaction | #algorithms #memes | Social Foraging | Scoop.it

This paper examines the stigmergic dimensions of online interactive creativity through the lens of Picbreeder. Picbreeder is a web-based system for collaborative interactive evolution of images. The Picbreeder applet starts by randomly generating several images, which are then mated and mutated based on the user’s selections. The user can then publish the image to the Picbreeder website where other users can download and continue the image’s evolution. Within this process, users collaboratively create imageswith significant complexity, all without explicit communication. In short, Picbreeder encourages a new form of stigmergic collaborative creation. The most surprising result of the Picbreeder experiment during more than 3 years of operation has been the quality of the  resulting images, despite the limited ways of interacting with other users. This fact challenges some commonly held notions of creativity, both online and offline. While current cognitive research in creativity places significant emphasis of the personal traits and cognitive structures that give rise to creative thought, Picbreeder highlights the potential for the emergence of creativity through stigmergic interaction. Picbreeder offers a rich data set for analysis of collaborative interaction with over 155,000 inputs from hundreds of users combined to create over 7500 images. It is hoped that the insights offered in this paper will influence both the understanding of collaborative creativity and the development of new modes of online creative interaction.


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luiy's curator insight, May 2, 2:56 PM

Creativity as stigmergy

 

If stigmergy happens when an agent’s effect on the environment “stimulates and guides” the work of others, then certainly creative communities must be subject to some kind of stigmergy. No creative endeavor exists in a vac- uum, and being inspired and stimulated by the work of another is so fundamental to creative communities of artists, academics, engineers, etc., that it is difficult to imagine these communities functioning any other way.

 

Closely related to the concept of stigmergy is the concept of self-organization. The reason that it is remarkable that one user’s work stimulates another’s is the emergence of patterns that appear as if that they could be centrally controlled. Often, a mix of direct communication and con- trol as well as emergent properties of the social structure give rise to collaborative creative activities. Fig. 4 suggests an informal ordering of the amount direct communication and coordination involved in several different types of creative processes, with emergent creative processes on the left end, and highly coordinated processes on the right

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


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Rescooped by Ashish Umre from Influence et contagion
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Competition among #memes in a world with limited attention

Competition among #memes in a world with limited attention | Social Foraging | Scoop.it
The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.

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luiy's curator insight, February 22, 8:06 AM

Here we outline a number of empirical findings that motivate both our question and the main assumptions behind our model. We then describe the proposed agent-based toy model of meme diffusion and compare its predictions with the empirical data. Finally we show that the social network structure and our finite attention are both key ingredients of the diffusion model, as their removal leads to results inconsistent with the empirical data.

 

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Limited attention


We first explore the competition among memes. In particular, we test the hypothesis that the attention of a user is somewhat independent from the overall diversity of information discussed in a given period. Let us quantify the breadth of attention of a user through Shannon entropy S = −Σi f(i) log f(i) where f(i) is the proportion of tweets generated by the user about meme i. Given a user who has posted n messages, her entropy can be as small as 0, if all of her posts are about the same meme; or as large as log n if she has posted a message about each of n different memes. We can measure the diversity of the information available in the system analogously, defining f(i) as the proportion of tweets about meme i across all users. Note that these entropy-based measures are subject to the limits of our operational definition of a meme; finer or coarser definitions would yield different values.

 

John Caswell's curator insight, March 2, 8:23 AM

Very intetesting! Attention spans!

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Algorithm Distinguishes Memes from Ordinary Information

Algorithm Distinguishes Memes from Ordinary Information | Social Foraging | Scoop.it
Network theorists have developed a way to identify the top memes in science and study how they evolved 

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luiy's curator insight, May 26, 5:04 AM

Memes are the cultural equivalent of genes: units that transfer ideas or practices from one human to another by means of imitation. In recent years, network scientists have become increasingly interested in how memes spread.

This kind of work has led to important insights into the nature of news cycles, into information avalanches on social networks and into the role that networks themselves play in this spreading process.

 

But what exactly makes a meme and distinguishes it from other forms of information is not well understood. Today, Tobias Kuhn at ETH Zurich in Switzerland and a couple of pals say they’ve developed a way to automatically distinguish scientific memes from other forms of information for the first time. And they’ve used this technique to find the most important ideas in physics and how they’ve evolved in the last 100 years.

 

The word ‘meme’ was coined by the evolutionary biologists Richard Dawkins in his 1976 book The Selfish Gene. He argued that ideas, melodies, behaviours and so on, all evolve in the same way as genes, by means of replication and mutation, but using human culture rather than biology as the medium of evolution.

  

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#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | Social Foraging | Scoop.it

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luiy's curator insight, March 27, 1:44 PM

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

António F Fonseca's curator insight, April 2, 6:01 AM

Another paper about popularity prediction.