Global Patterns of Knowledge: Language, Genre, and the Geography of Knowledge

TL;DR

This paper uses economic complexity analysis to explore how different language communities contribute to Wikipedia, revealing diverse knowledge production modes influenced by cultural and geopolitical factors, which impact AI data biases.

Contribution

It introduces a novel application of economic complexity analysis to Wikipedia editing history, uncovering the diversity and geopolitical influences on knowledge production across languages.

Findings

Distinct cultural specializations in language communities

Knowledge production modes reflect geopolitical boundaries

Standardized topics show common production patterns

Abstract

Online platforms, particularly Wikipedia, have become critical infrastructures for providing diverse linguistic and cultural contexts. This human-curated knowledge now forms the foundation for modern AI. However, we have not yet fully explored how knowledge production capability vary across languages and domains. Here, we address this gap by applying economic complexity analysis to understand the editing history of Wikipedia platforms. This approach allows us to infer the latent mode of ``knowledge-production'' of each language community from the diversity and specialization of its contributed content. We reveal that different language communities exhibit distinct specializations, particularly in cultural subjects. Furthermore, we map the global landscape of these production modes, finding that the structure of knowledge production strongly reflects geopolitical boundaries. Our findings suggest that while a common mode of knowledge production exists for standardized topics such as science, it is more diverse for cultural topics or controversial subjects such as conspiracy theories. The association between differences in knowledge production capability and geopolitical factors implies how linguistic and cultural dynamics shape our worldview and the biases embedded in Wikipedia data, a unique, massive, and essential dataset for modern AI.