Experimental Measures of News Personalization in Google News

  • Vittoria Cozza
  • Van Tien Hoang
  • Marinella Petrocchi
  • Angelo Spognardi
Conference paper

DOI: 10.1007/978-3-319-46963-8_8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9881)
Cite this paper as:
Cozza V., Hoang V.T., Petrocchi M., Spognardi A. (2016) Experimental Measures of News Personalization in Google News. In: Casteleyn S., Dolog P., Pautasso C. (eds) Current Trends in Web Engineering. ICWE 2016. Lecture Notes in Computer Science, vol 9881. Springer, Cham

Abstract

Search engines and social media keep trace of profile- and behavioral-based distinct signals of their users, to provide them personalized and recommended content. Here, we focus on the level of web search personalization, to estimate the risk of trapping the user into so called Filter Bubbles. Our experimentation has been carried out on news, specifically investigating the Google News platform. Our results are in line with existing literature and call for further analyses on which kind of users are the target of specific recommendations by Google.

Keywords

Filter bubbles Web search results News publishers 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Vittoria Cozza
    • 1
    • 2
  • Van Tien Hoang
    • 3
  • Marinella Petrocchi
    • 1
  • Angelo Spognardi
    • 1
    • 4
  1. 1.IIT-CNRPisaItaly
  2. 2.DEIPolytechnic University of BariBariItaly
  3. 3.IMT School for Advanced StudiesLuccaItaly
  4. 4.DTU ComputeLyngbyDenmark

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