Social Foraging
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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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Rescooped by Ashish Umre from Influence et contagion
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Detecting Automation of Twitter Accounts Are You a Human, #Bot, or #Cyborg? | #contagion


Via luiy
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luiy's curator insight, June 12, 2014 2:23 PM

We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts:

 

1) an entropy-based component,

 

2) a spam detection component,


3) an account properties component, and


4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot, or cyborg.


Our experimental evaluation demonstrates the efficacy of the proposed classification system

Rescooped by Ashish Umre from Influence et contagion
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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 

Via luiy
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luiy's curator insight, May 26, 2014 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.

  

Rescooped by Ashish Umre from Influence et contagion
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The GLEAM Simulator system | #dataviz #complexity #prediction #visualisation

The GLEAM Simulator system  | #dataviz #complexity #prediction #visualisation | Social Foraging | Scoop.it

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.


Via luiy
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luiy's curator insight, June 7, 2014 5:03 AM

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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#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, 2014 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, 2014 6:01 AM

Another paper about popularity prediction.