Highlighting Entanglement of Cultures via Ranking of Multilingual Wikipedia Articles

TL;DR

This paper analyzes cultural differences and similarities by ranking multilingual Wikipedia articles using network algorithms, revealing entanglements and distinctions among cultures through the distribution of notable persons.

Contribution

It introduces a novel network-based ranking approach to compare cultural prominence across multiple Wikipedia language editions, highlighting cultural interconnections.

Findings

Local heroes dominate in individual editions

Global heroes are also identified across editions

Network analysis shows Zipf law distribution in cultural rankings

Abstract

How different cultures evaluate a person? Is an important person in one culture is also important in the other culture? We address these questions via ranking of multilingual Wikipedia articles. With three ranking algorithms based on network structure of Wikipedia, we assign ranking to all articles in 9 multilingual editions of Wikipedia and investigate general ranking structure of PageRank, CheiRank and 2DRank. In particular, we focus on articles related to persons, identify top 30 persons for each rank among different editions and analyze distinctions of their distributions over activity fields such as politics, art, science, religion, sport for each edition. We find that local heroes are dominant but also global heroes exist and create an effective network representing entanglement of cultures. The Google matrix analysis of network of cultures shows signs of the Zipf law distribution. This approach allows to examine diversity and shared characteristics of knowledge organization between cultures. The developed computational, data driven approach highlights cultural interconnections in a new perspective.