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Patterns in Large Data Show How Information Travels


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Each coloured circle shows a group of countries that have similar information interests. The links show the connections between the groups.

Researchers at Umea University have determined that an analysis of how people edit content on Wikipedia can reveal what information matters to them, and with whom they have most in common.

Credit: Fariba Karimi

Umea University researchers have found an analysis of how people edit content on Wikipedia can reveal what information matters to them, and with whom they have most in common.

The study's results show people care most about local and regional information related to sports, media, celebrities, or local pages.

In addition, people from countries with similar languages or historic backgrounds care about similar information, according to the researchers.

"We can project these similarities in a network where countries are nodes and links represent the strengths of similarities," says Umea network scientist Fariba Karimi.

She says it is possible to analyze how information, diseases, or financial crises spread over networks by extracting patterns from raw datasets of social and economical interactions. "These social interactions happen in time and, depending on the time, the influence might differ," Karimi says.

The researchers found that accounting for time makes the spreading dynamic different compared to static networks. "This is important, because it can help us to better understand the spreading processes in real social systems that are mostly dynamic and change over time," Karimi says.

From Umea University (Sweden)
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Abstracts Copyright © 2015 Information Inc., Bethesda, Maryland, USA


 

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