ArticlePloS one2015
Interactions of cultures and top people of Wikipedia from ranking of 24 language editions.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Article
- Collective intelligence defines biological functions in Wikipedia as communities in the hidden protein connection network.PLoS computational biology · 2020Article
- Gender Differences in Familiar Face Recognition and the Influence of Sociocultural Gender Inequality.Scientific reports · 2019Article
- Interactions of pharmaceutical companies with world countries, cancers and rare diseases from Wikipedia network analysis.PloS one · 2019Article
- Capturing the influence of geopolitical ties from Wikipedia with reduced Google matrix.PloS one · 2018Article
- Robust clustering of languages across Wikipedia growth.Royal Society open science · 2017Article
- Tail-scope: Using friends to estimate heavy tails of degree distributions in large-scale complex networks.Scientific reports · 2015Article
- Article
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Authors and funding
6 authors.
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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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