Interactions of cultures and top people of Wikipedia from ranking of 24 language editions

TL;DR

This study analyzes Wikipedia's hyperlink networks across 24 language editions using PageRank and related algorithms to explore cultural interactions, historical figures, and their geographical and temporal distributions over 35 centuries.

Contribution

It introduces a novel method combining network analysis and automatic name extraction to study cultural interrelations through Wikipedia's structure.

Findings

Existence of local and global historical figures in Wikipedia editions.

Distribution patterns of figures across time and geography.

Identification of influential cultures based on inter-culture interaction networks.

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.