Wikipedia Reader Navigation: When Synthetic Data Is Enough

TL;DR

This study evaluates how effectively Wikipedia's publicly available clickstream data can approximate actual reader navigation patterns, demonstrating its utility for research while respecting user privacy.

Contribution

The paper systematically compares real Wikipedia navigation sequences with synthetic ones generated from clickstream data, showing close approximation with small effect sizes.

Findings

Clickstream data closely approximates real navigation with less than 10% difference.

Synthetic sequences can be used for practical downstream applications.

Clickstream data enables privacy-preserving research on user navigation.

Abstract

Every day millions of people read Wikipedia. When navigating the vast space of available topics using hyperlinks, readers describe trajectories on the article network. Understanding these navigation patterns is crucial to better serve readers' needs and address structural biases and knowledge gaps. However, systematic studies of navigation on Wikipedia are hindered by a lack of publicly available data due to the commitment to protect readers' privacy by not storing or sharing potentially sensitive data. In this paper, we ask: How well can Wikipedia readers' navigation be approximated by using publicly available resources, most notably the Wikipedia clickstream data? We systematically quantify the differences between real navigation sequences and synthetic sequences generated from the clickstream data, in 6 analyses across 8 Wikipedia language versions. Overall, we find that the differences between real and synthetic sequences are statistically significant, but with small effect sizes, often well below 10%. This constitutes quantitative evidence for the utility of the Wikipedia clickstream data as a public resource: clickstream data can closely capture reader navigation on Wikipedia and provides a sufficient approximation for most practical downstream applications relying on reader data. More broadly, this study provides an example for how clickstream-like data can generally enable research on user navigation on online platforms while protecting users' privacy.