Mapping bilateral information interests using the activity of Wikipedia editors

TL;DR

This paper develops a scalable statistical model to map global information interests using Wikipedia editor activity, revealing 18 interest-based clusters influenced mainly by language and religion, and providing insights into global information exchange patterns.

Contribution

It introduces a novel scalable method to quantify and visualize bilateral information interests between countries based on Wikipedia activity, identifying key factors like language and religion.

Findings

Countries form 18 clusters with similar interests

Language and religion are primary factors influencing bilateral ties

Method can track changes in global information exchange over time

Abstract

We live in a global village where electronic communication has eliminated the geographical barriers of information exchange. The road is now open to worldwide convergence of information interests, shared values, and understanding. Nevertheless, interests still vary between countries around the world. This raises important questions about what today's world map of in- formation interests actually looks like and what factors cause the barriers of information exchange between countries. To quantitatively construct a world map of information interests, we devise a scalable statistical model that identifies countries with similar information interests and measures the countries' bilateral similarities. From the similarities we connect countries in a global network and find that countries can be mapped into 18 clusters with similar information interests. Through regression we find that language and religion best explain the strength of the bilateral ties and formation of clusters. Our findings provide a quantitative basis for further studies to better understand the complex interplay between shared interests and conflict on a global scale. The methodology can also be extended to track changes over time and capture important trends in global information exchange.