Interactions of Cultures and Top People of Wikipedia from Ranking of 24 Language Editions

This week, our latest article with Young-Ho Eom, David Laniado, Andreas Kaltenbrunner, Sebastiano Vigna & Dima L. Shepelyansky has been published in the open-access journal PLOS ONE (full article here).

In this paper, we apply methods of Markov chains and Google matrix on 24 editions of Wikipedia that cover languages which played an important role in human history and include Western, Asian and Arabic cultures. We find spatial, temporal, and gender skewness in Wikipedia. Each language edition highlights local figures so that most of its own historical figures are born in the countries which use the language of the edition. In summary, we believe that the analysis of historical figures can be a useful step towards the understanding of local and global history and interactions of world cultures.

pagerank

Birth place distributions over countries of the top historical figures of 24 Wikipedia edition, according to PageRank.

Abstract:

Wikipedia is a huge global repository of human knowledge that can be leveraged to investigate interwinements between cultures. With this aim, we 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, we obtain the top 100 historical figures, for each edition and for each algorithm. We investigate their spatial, temporal, and gender distributions in dependence of their cultural origins. Our 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, we 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. We 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, we construct a network of cultures and identify the most influential cultures according to this network.

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