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)

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Demartini, G., Siersdorfer, S.: Dear Search Engine: What’s your opinion about...? - Sentiment Analysis for Semantic Enrichment of Web Search Results. In: Semantic Search 2010 Workshop located at the 19th Int. WWW 2010 (2010)Google Scholar
  2. 2.
    Diaz, F.: Integration of news content into web results. In: WSDM 2009. ACM, New York (2009)Google Scholar
  3. 3.
    Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: ACL 2005. Association for Computational Linguistics, Stroudsburg (2005)Google Scholar
  4. 4.
    Fisichella, M., Stewart, A., Denecke, K., Nejdl, W.: Unsupervised public health event detection for epidemic intelligence. In: CIKM 2010, New York, NY, USA (2010)Google Scholar
  5. 5.
    Galutung, J., Ruge, M.H.: The structure of foreign news. the presentation of the congo, cuba and cyprus crises in four foreign newspapers. Journal of Peace Research 2, 64–91 (1965)CrossRefGoogle Scholar
  6. 6.
    Hall, M.: Correlation-based Feature Selection for Machine Learning (1998)Google Scholar
  7. 7.
    Hu, M., Sun, A., Lim, E.-P.: Event detection with common user interests. In: Proceeding of the 10th ACM Workshop on Web Information and Data Management, WIDM 2008. ACM, New York (2008)Google Scholar
  8. 8.
    Krestel, R., Mehta, B.: Predicting news story importance using language features. In: Web Intelligence, pp. 683–689. IEEE (2008)Google Scholar
  9. 9.
    Li, Z., Wang, B., Li, M., Ma, W.-Y.: A Probabilistic Model for Retrospective News Event Detection. In: SIGIR, pp. 106–113 (2005)Google Scholar
  10. 10.
    Lin, C.X., Zhao, B., Mei, Q., Han, J.: Pet: a statistical model for popular events tracking in social communities. In: KDD 2010. ACM, New York (2010)Google Scholar
  11. 11.
    Mizzaro, S.: Relevance: The whole history. JASIS 48(9), 810–832 (1997)CrossRefGoogle Scholar
  12. 12.
    Patton, R.M., Potok, T.E.: Identifying event impacts by monitoring the news media. In: Information Visualisation, IV 2008 (July 2008)Google Scholar
  13. 13.
    Patton, R.M., Treadwell, J.N., Kerekes, R.A., Potok, T.E.: Discovery, analysis, and characteristics of event impacts. In: Information Fusion 2008 (June 2008)Google Scholar
  14. 14.
    San Pedro, J., Siersdorfer, S.: Ranking and classifying attractiveness of photos in folksonomies. In: WWW 2009. ACM, New York (2009)Google Scholar
  15. 15.
    Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K.: Improvements to the smo algorithm for svm regression. IEEE Transactions on Neural Networks 11(5), 1188–1193 (2000)CrossRefGoogle Scholar
  16. 16.
    Soboroff, I.: Overview of the trec 2004 novelty track. In: Voorhees, E.M., Buckland, L.P. (eds.) TREC, Special Publication 500-261. National Institute of Standards and Technology (NIST) (2004)Google Scholar
  17. 17.
    Soboroff, I., Harman, D.: Overview of the trec 2003 novelty track. In: TREC (2003)Google Scholar
  18. 18.
    Staab, J.F.: The role of news factors in news selection: A theoretical reconsideration. European Journal of Communication 5, 423–443 (1990)CrossRefGoogle Scholar
  19. 19.
    Tsagkias, M., Weerkamp, W., de Rijke, M.: Predicting the volume of comments on online news stories. In: CIKM 2009. ACM, New York (2009)Google Scholar
  20. 20.
    Verhoeven, P.: Sound-bite science: On the brevity of science and scientific experts in western european television news. Science Communication 32(3), 330–335 (2010)CrossRefGoogle Scholar
  21. 21.
    Zhang, K., Zi, J., Wu, L.G.: New event detection based on indexing-tree and named entity. In: SIGIR 2007. ACM, New York (2007)Google Scholar

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

Personalised recommendations