Inspiration, Captivation, and Misdirection: Emergent Properties in Networks of Online Navigation

TL;DR

This paper analyzes Wikipedia's navigation patterns by modeling it as a network, revealing how information structure influences user behavior and suggesting ways to improve online information access.

Contribution

It introduces a novel phase space based on article traffic parameters to classify Wikipedia articles and understand navigation dynamics.

Findings

Different article types occupy distinct regions in the navigational phase space.

The structure of Wikipedia influences user navigation patterns.

Applications include identifying and correcting network deficiencies.

Abstract

The World Wide Web (WWW) has fundamentally changed the ways billions of people are able to access information. Thus, understanding how people seek information online is an important issue of study. Wikipedia is a hugely important part of information provision on the web, with hundreds of millions of users browsing and contributing to its network of knowledge. The study of navigational behaviour on Wikipedia, due to the site's popularity and breadth of content, can reveal more general information seeking patterns that may be applied beyond Wikipedia and the Web. Our work addresses the relative shortcomings of existing literature in relating how information structure influences patterns of navigation online. We study aggregated clickstream data for articles on the English Wikipedia in the form of a weighted, directed navigational network. We introduce two parameters that describe how articles act to source and spread traffic through the network, based on their in/out strength and entropy. From these, we construct a navigational phase space where different article types occupy different, distinct regions, indicating how the structure of information online has differential effects on patterns of navigation. Finally, we go on to suggest applications for this analysis in identifying and correcting deficiencies in the Wikipedia page network that may also be adapted to more general information networks.