Difference between revisions of "Interactions of Cultures and Top People of Wikipedia from Ranking of 24 Language"

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'''Interactions of Cultures and Top People of Wikipedia from Ranking of 24 Language''' - scientific work related to Wikipedia quality published in 2015, written by Young-Ho Eom, Pablo Aragón, David Laniado, Andreas Kaltenbrunner, Sebastiano Vigna, Dima L. Shepelyansky, Kaltenbrunner A, Vigna S and Dima L. Shepelyansky.
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'''Interactions of Cultures and Top People of Wikipedia from Ranking of 24 Language''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Young-Ho Eom]], [[Pablo Aragón]], [[David Laniado]], [[Andreas Kaltenbrunner]], [[Sebastiano Vigna]], [[Dima L. Shepelyansky]], [[Kaltenbrunner A]], [[Vigna S]] and [[Dima L. Shepelyansky]].
  
 
== Overview ==
 
== Overview ==
Wikipedia is a huge global repository of human knowledge that can be leveraged to investigate interwinements between cultures. With this aim, authors apply methods of Markov chains and Google matrix for the analysis of the hyperlink networks of 24 Wikipedia language editions, and rank all their articles by PageRank, 2DRank and CheiRank algorithms. Using automatic extraction of people names, authors obtain the top 100 historical figures, for each edition and for each algorithm. Authors investigate their spatial, temporal, and gender distributions in dependence of their cultural origins. Authors study demonstrates not only the existence of skewness with local figures, mainly recognized only in their own cultures, but also the existence of global historical figures appearing in a large number of editions. By determining the birth time and place of these persons, authors perform an analysis of the evolution of such figures through 35 centuries of human history for each language, thus recovering interactions and entanglement of cultures over time. Authors also obtain the distributions of historical figures over world countries, highlighting geographical aspects of cross-cultural links. Considering historical figures who appear in multiple editions as interactions between cultures, authors construct a network of cultures and identify the most influential cultures according to this network.
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Wikipedia is a huge global repository of human knowledge that can be leveraged to investigate interwinements between cultures. With this aim, authors apply methods of Markov chains and [[Google]] matrix for the analysis of the hyperlink networks of 24 [[Wikipedia]] language editions, and rank all their articles by PageRank, 2DRank and CheiRank algorithms. Using automatic extraction of people names, authors obtain the top 100 historical figures, for each edition and for each algorithm. Authors investigate their spatial, temporal, and gender distributions in dependence of their cultural origins. Authors study demonstrates not only the existence of skewness with local figures, mainly recognized only in their own cultures, but also the existence of global historical figures appearing in a large number of editions. By determining the birth time and place of these persons, authors perform an analysis of the evolution of such figures through 35 centuries of human history for each language, thus recovering interactions and entanglement of cultures over time. Authors also obtain the distributions of historical figures over world countries, highlighting geographical aspects of cross-cultural links. Considering historical figures who appear in multiple editions as interactions between cultures, authors construct a network of cultures and identify the most influential cultures according to this network.

Revision as of 20:54, 16 November 2020

Interactions of Cultures and Top People of Wikipedia from Ranking of 24 Language - scientific work related to Wikipedia quality published in 2015, written by Young-Ho Eom, Pablo Aragón, David Laniado, Andreas Kaltenbrunner, Sebastiano Vigna, Dima L. Shepelyansky, Kaltenbrunner A, Vigna S and Dima L. Shepelyansky.

Overview

Wikipedia is a huge global repository of human knowledge that can be leveraged to investigate interwinements between cultures. With this aim, authors apply methods of Markov chains and Google matrix for the analysis of the hyperlink networks of 24 Wikipedia language editions, and rank all their articles by PageRank, 2DRank and CheiRank algorithms. Using automatic extraction of people names, authors obtain the top 100 historical figures, for each edition and for each algorithm. Authors investigate their spatial, temporal, and gender distributions in dependence of their cultural origins. Authors study demonstrates not only the existence of skewness with local figures, mainly recognized only in their own cultures, but also the existence of global historical figures appearing in a large number of editions. By determining the birth time and place of these persons, authors perform an analysis of the evolution of such figures through 35 centuries of human history for each language, thus recovering interactions and entanglement of cultures over time. Authors also obtain the distributions of historical figures over world countries, highlighting geographical aspects of cross-cultural links. Considering historical figures who appear in multiple editions as interactions between cultures, authors construct a network of cultures and identify the most influential cultures according to this network.