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The Collective Direction of Attention Diffusion.

Cheng-Jun Wang1, Lingfei Wu2, Jiang Zhang3

  • 1Computational Communication Collaboratory, School of Journalism and Communication, Nanjing University, Nanjing 210093, P.R. China.

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Summary

Web browsing forms a directed, tree-like structure, revealing time-sensitive user behavior. Clickstream network analysis shows stable patterns and user-click scaling, explaining news story popularity dynamics.

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Area of Science:

  • Network Science
  • Web Analytics
  • Information Science

Background:

  • Understanding user attention flow on the web is crucial for content dissemination.
  • Clickstream data offers insights into user navigation patterns and content popularity.

Purpose of the Study:

  • To analyze the structure of web attention flow using clickstream data.
  • To investigate the relationship between news story attributes (clicks, age) and user browsing behavior.
  • To model the dynamics of news story popularity from a network perspective.

Main Methods:

  • Construction of clickstream networks from news website data, with nodes as stories and edges as consecutive clicks.
  • Definition and calculation of 'flow distance' (Li) to determine clickstream direction.
  • Analysis of relationships between flow distance (Li), clicks (Ci), and story age (Ti).
  • Examination of network stability and user-click scaling across 1,000 web forums.

Main Results:

  • Clickstream networks exhibit a directed, tree-like structure.
  • Flow distance, clicks, and story age are interconnected, explaining news story lifecycles.
  • Clickstream networks demonstrate stable structures over time, leading to universal user-click scaling.
  • Empirical data aligns with simulation results from individual browsing behavior models.

Conclusions:

  • The stable, tree-like structure of web clickstreams reflects time-sensitive user browsing preferences.
  • Network analysis provides a framework for understanding the rise and decay of online content.
  • User browsing behavior exhibits predictable patterns that can be modeled and understood through network structures.