Predicting the Future Impact of News Events

  • Julien Gaugaz
  • Patrick Siehndel
  • Gianluca Demartini
  • Tereza Iofciu
  • Mihai Georgescu
  • Nicola Henze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)


The amount of news content on the Web is increasing, enabling users to access news articles coming from a variety of sources: from newswires, news agencies, blogs, and at various places, e.g. even within Web search engines result pages. Anyhow, it still is a challenge for current search engines to decide which news events are worth being shown to the user (either for a newsworthy query or in a news portal). In this paper we define the task of predicting the future impact of news events. Being able to predict event impact will, for example, enable a newspaper to decide whether to follow a specific event or not, or a news search engine which stories to display. We define a flexible framework that, given some definition of impact, can predict its future development at the beginning of the event. We evaluate several possible definitions of event impact and experimentally identify the best features for each of them.


Aggregation Method News Article Event Impact Partial Event Future Impact 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Julien Gaugaz
    • 1
  • Patrick Siehndel
    • 1
  • Gianluca Demartini
    • 2
  • Tereza Iofciu
    • 3
  • Mihai Georgescu
    • 1
  • Nicola Henze
    • 1
  1. 1.L3S Research CenterGermany
  2. 2.eXascale InfolabUniversity of FribourgSwitzerland
  3. 3.XING AGGermany

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