e-Xploration
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e-Xploration
antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
Curated by luiy
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Which social media network type is your topic? Which did you want it to be? | #SNA #datascience

Which social media network type is your topic?  Which did you want it to be? | #SNA #datascience | e-Xploration | Scoop.it
There are at least six different types of social media network structures present in systems like Twitter and other services in which people are able to reply to one another. Each of the six patter...
luiy's insight:

This table describes each of the six patterns in terms of the difference between that pattern and the other five patterns.

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selfiecity : Investigating the style of self-portraits (selfies) | #dataviz #research

selfiecity : Investigating the style of self-portraits (selfies) | #dataviz #research | e-Xploration | Scoop.it

Investigating the style of self-portraits (selfies) in five cities across the world.

luiy's insight:

Investigating the style of self-portraits (selfies) in five cities across the world.

 

Selfiecity investigates selfies using a mix of theoretic, artistic and quantitative methods:

 

We present our findings about the demographics of people taking selfies, their poses and expressions. 

 

Rich media visualizations (imageplots) assemble thousands of photos to reveal interesting patterns. 

 

The interactive selfiexploratory allows you to navigate the whole set of 3200 photos. 

 

Finally, theoretical essays discuss selfies in the history of photography, the functions of images in social media, and methods and dataset.

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Open source diagramming framework for Java | Datagr4m | #SNA #clustering

Open source diagramming framework for Java | Datagr4m | #SNA #clustering | e-Xploration | Scoop.it
luiy's insight:

Assigning layouts to structural data patterns generates diagrams closed to the domain model conventions.

2 ways for analysing data topologies:

 

1- top-down analysis: compute largest super-structures first, and refine content of each structure by computing internal sub-structures. 

 

2- bottom-up analysis: compute smallest sub-structures first, and then generate super-structures based on sub-structures patterns until no more super-structure is generated.

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Looking at Culture through a Big Data Lens I #patterns #memes

Looking at Culture through a Big Data Lens I #patterns #memes | e-Xploration | Scoop.it
I’m excited about all the (possible) breakthroughs we see happening in cultural research.
luiy's insight:

Predicting by looking at narratives


Other Big Data cultural research examples will be presented tomorrow at a conference organized by the Dutch Meertens Institute “Patterns in narrative texts“. The data that will be discussed range from narrative journalistic texts to orally transmitted folktales. In the study of history, diachronic corpora can be mined to discover how historical events are reflected in language use. In folk narrative research, patterns of interest include the stability and variability of ‘narrative building blocks’ (motifs, memes) in oral transmission, and geographical dispersion of folk beliefs in the supernatural. Establishing links between narrative texts is a common factor in all this research.

 

One of the pieces of research that will be discussed is “Mining the Twentieth Century’s History from the TIME Magazine Corpus”. Mike Kestemont & Folgert Karsdorp are going to explain how to predict Times’s Person of the Year. In their research they have paid special attention to the intriguing interplay between this list of influential personalities and the manner in which they are discussed in the magazine’s own archive. They will have a lot to explain, looking at their top-10 list for 2013, since they’ve missed the person that has won this year, Pope Fransicus. But still the researchers have a hit-rate of more than 20%.

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Rescooped by luiy from Social Network Analysis #sna
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Barabási : The Hidden #Pattern Behind Everything We Do I #datascience #SNA


Via ukituki
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ukituki's curator insight, November 24, 2013 8:35 AM

Barabasi argues that, rather than being random,
humans actually act in predictable patterns. We go along for long
periods of quiet routine followed suddenly by loud bursts of activity.
Barabasi demonstrates that these breaks in routine, or "bursts," are
present in all aspects of our existence— in the way we write emails,
spend our money, manage our health, form ideas. Barabasi has even
found "burstiness" in our webpage clicking activity and the online
news cycle."

Rescooped by luiy from 7wData
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#Data-Mining Our Dreams | #patterns

#Data-Mining Our Dreams | #patterns | e-Xploration | Scoop.it
New York Times
Data-Mining Our Dreams
New York Times
Calkins used fairly simple tools and a small data set to identify patterns in dream content, but later studies have largely confirmed these insights and extended them to new groups of people.

Via Yves Mulkers
luiy's insight:

The emergence of modern digital-search technology has raised the intriguing possibility of pushing Calkins’s rather slow and labor-intensive approach to new levels of speed and sophistication. What if the coding categories she and others have used could be transformed into computer algorithms that automatically analyze not just hundreds but thousands or even millions of dreams? What new patterns and subtler dimensions of meaning might we identify?

To take the first step in exploring that possibility I have conducted several experiments in “blind analysis,” a technique developed with the help of the psychologist G. William Domhoff at the University of California, Santa Cruz.

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Rescooped by luiy from visual data
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A Visualization of Global “Brain Drain” in Science Inspired by Abstract Art

A Visualization of Global “Brain Drain” in Science Inspired by Abstract Art | e-Xploration | Scoop.it

Mapping the global flow of scientific talent by way of Mondrian and Kandinsky.


After their wonderful visual timeline of the future based on famous fiction and visual history of the Nobel Prize, Italian information visualization designer Giorgia Lupi and her team at Accurat are back with another exclusive English version of a piece originally designed for La Lettura, the Sunday literary supplement of an Italian newspaper— this time exploring the phenomenon of global “brain drain” in science, with an eye towards understanding the reasons why researchers might choose to leave their countries of origin and pursue careers elsewhere.


Via Lauren Moss
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#Truthy : Information diffusion research | #political #memes #patterns #SNA

#Truthy : Information diffusion research | #political #memes #patterns #SNA | e-Xploration | Scoop.it
luiy's insight:

Information diffusion research at Indiana University

 

Truthy is a research project that helps you understand how communication spreads on Twitter. 

 

We currently focus on tweets about politics, social movements and news.

 

 

Political Topics

Interactive visualizations of U.S. political conversation on Twitter :

 

- How does sentiment change over time in response to political events?

- What is most popular over time?

- Who are the most influential users?

- How does information spread in the social network?

 

 

Sentiment Timeline

- How does sentiment change over time in response to political events?

 

 

Gallery Descriptions of interesting memes:  http://truthy.indiana.edu/gallery

 

 

Meme Patterns:

What other memes are related to this one?  http://truthy.indiana.edu/memedetail?id=783&resmin=45&theme_id=4

 

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Rescooped by luiy from Data is big
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#Open Software for Text Analysis, Text Mining, Text Analytics | #clustering #patterns

#Open Software for Text Analysis, Text Mining, Text Analytics | #clustering #patterns | e-Xploration | Scoop.it
Review of Top 11 Free Software for Text Analysis, Text Mining, Text Analytics ? KH Coder, Carrot2, GATE, tm, Gensim, Natural Language Toolkit, RapidMiner, Unstructured Information Management Architecture, OpenNLP, KNIME, Orange-Textable and LPU are some of the key vendors who provides text analytics software

Via ukituki
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Divided Edge Bundling for Directional Network Data I #SNA #dataviz

Divided Edge Bundling for Directional Network Data I #SNA #dataviz | e-Xploration | Scoop.it
luiy's insight:

The node-link diagram is an intuitive and venerable way to depict a graph. To reduce clutter and improve the readability of node-link views, Holten & van Wijk's force-directed edge bundling employs a physical simulation to spatially group graph edges. While both useful and aesthetic, this technique has shortcomings: it bundles spatially proximal edges regardless of direction, weight, or graph connectivity. As a result, high-level directional edge patterns are obscured. We present divided edge bundling to tackle these shortcomings. By modifying the forces in the physical simulation, directional lanes appear as an emergent property of edge direction. By considering graph topology, we only bundle edges related by graph structure. Finally, we aggregate edge weights in bundles to enable more accurate visualization of total bundle weights. We compare visualizations created using our technique to standard force-directed edge bundling, matrix diagrams, and clustered graphs; we find that divided edge bundling leads to visualizations that are easier to interpret and reveal both familiar and previously obscured patterns.

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Words that signal problems with understanding are similar across languages | #sentiments #patterns #semantic

Words that signal problems with understanding are similar across languages | #sentiments #patterns #semantic | e-Xploration | Scoop.it
A word like 'Huh?'—used when one has not caught what someone just said—appears to be universal: it is found to have very similar form and function in languages across the globe.

Via Wildcat2030
luiy's insight:

It might seem frivolous to carry out scientific research on a word like 'Huh?' But in fact this little word is an indispensable tool in human communication. Without words like this we would be unable to signal when we have problems with hearing or understanding what was said, and our conversations would be constantly derailed by communicative mishaps. The research is part of a larger investigation of language and social interaction funded by the European Research Council.

Read more at: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0078273

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Atotsm's curator insight, November 17, 2013 12:47 PM

One of the oldest signals is pain. Aaaaaa and understanding aaaaaa. Ahaaa!! Hahahaha :)

Rescooped by luiy from Open Data Sets
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The #sierpinski triangle page to end most sierpinski triangle pages | #patterns #fractals #dataviz

The #sierpinski triangle page to end most sierpinski triangle pages | #patterns #fractals #dataviz | e-Xploration | Scoop.it

Via Claudia Mihai
luiy's insight:
Constructing the Sierpinski triangle

Throughout my years playing around with fractals, the Sierpinski triangle has been a consistent staple. The triangle is named after Wacław Sierpiński and as fractals are wont the pattern appears in many places, so there are many different ways of constructing the triangle on a computer.

All of the methods are fundamentally iterative. The most obvious method is probably the triangle-in-triangle approach. We start with one triangle, and at every step we replace each triangle with 3 subtriangles:

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