The Decentralized Structure of Collective Attention on the Web
Lingfei Wu, Jiang Zhang

TL;DR
This paper analyzes the web's collective attention flow, revealing a decentralized structure where smaller sites disproportionately influence user attention, characterized by a specific scaling relationship between site impact and traffic.
Contribution
It uncovers a nontrivial scaling regularity in the web's attention network, demonstrating a decentralized structure favoring smaller websites.
Findings
Impact increases slower than traffic with a sublinear scaling exponent.
Web structure favors small sites in redirecting user attention.
Scaling relationship characterizes the decentralized nature of web attention flow.
Abstract
Background: The collective browsing behavior of users gives rise to a flow network transporting attention between websites. By analyzing the structure of this network we uncovered a nontrivial scaling regularity concerning the impact of websites. Methodology: We constructed three clickstreams networks, whose nodes were websites and edges were formed by the users switching between sites. We developed an indicator Ci as a measure of the impact of site i and investigated its correlation with the traffic of the site Ai both on the three networks and across the language communities within the networks. Conclusions: We found that the impact of websites increased slower than their traffic. Specifically, there existed a scaling relationship between Ci and Ai with an exponent gamma smaller than 1. We suggested that this scaling relationship characterized the decentralized structure of the…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Misinformation and Its Impacts
