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Visualising Related Entries in #Wikipedia Using #Gephi | #tutorial #SNA

Visualising Related Entries in #Wikipedia Using #Gephi | #tutorial #SNA | e-Xploration | Scoop.it
Sometime last week, @mediaczar tipped me off to a neat recipe on the wonderfully named Drunks&Lampposts blog, Graphing the history of philosophy, that uses Gephi to map an influence network in ...
luiy's insight:

To get DBpedia data into Gephi, we need to do three things:

 

- tell the importer where to find the data by giving it a URL (the “Driver” configuration setting);


- tell the importer what data we want to get back, by specifying what is essentially a database query (the “Request” configuration setting);


- tell Gephi how to create the network we want to visualise from the data returned from DBpedia (in the context of the “Request” configuration).

 

Fortunately, we don’t have to work out how to do this from scratch – from the Semantic Web ImportConfiguration panel, configure the importer by setting the configuration to DBPediaMovies.

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Infographic: An Amazing, Invisible Truth About Wikipedia

Infographic: An Amazing, Invisible Truth About Wikipedia | e-Xploration | Scoop.it

Every Wikipedia entry has an optional feature we take for granted--geotagging. An entry on the Lincoln Memorial will be linked to its specific latitude and longitude in Washington D.C. On any individual post, this may or may not be a useful thing. But what about looking at these locations en masse?

That was a question asked by data viz specialist and programmer Olivier Beauchesne. To find out, he downloaded all of Wikipedia (it’s open-source, after all) then used an algorithm that would assemble 300 topical clusters from popular, related keywords. Then he placed the location of each article in these topical clusters on a map. What he found was astounding...


Via Lauren Moss
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Rescooped by luiy from The New Global Open Public Sphere
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Essay of the Day: #CollectiveIntelligence and Neutral Point of View in the Case of Wikipedia I #CI


Via Pierre Levy
luiy's insight:

We examine whether collective intelligence helps achieve a neutral point of view (NPOV) using data from Wikipedia’s articles on US politics. Our null hypothesis builds on Linus’ Law, often expressed as “Given enough eyeballs, all bugs are shallow.” Our findings are consistent with a narrow interpretation of Linus’ Law, namely, a greater number of contributors to an article makes an article more neutral. No evidence supports a broad interpretation of Linus’ Law. Moreover, several empirical facts suggest the law does not shape many articles. The majority of articles receive little attention, and most articles change only mildly from their initial slant. Our study provides the first empirical evidence on the limit of collective intelligence. While many managers believe that they could improve their products by taking advantage of the wisdom of crowds, we show that in the case of Wikipedia, there are aspects such as NPOV that collective intelligence does not help achieve successfully.

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Frederic DOMON's curator insight, November 17, 2013 2:28 PM

Our study provides the first empirical evidence on the limit of collective intelligence. While many managers believe that they could improve their products by taking advantage of the wisdom of crowds, we show that in the case of Wikipedia, there are aspects such as NPOV that collective intelligence does not help achieve successfully.

Paz Barceló's curator insight, November 18, 2013 6:18 AM

Sobre los límites de la inteligencia colectiva.

Rick Frank's curator insight, November 18, 2013 9:34 AM

Interesting idea, but OMG this is boring to read, needs some STYLE.