Thematic Indicators Derived from World News Reports

  • Clive Best
  • Erik Van der Goot
  • Monica de Paola
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3495)


A method for deriving statistical indicators from the Europe Media Monitor (EMM) is described. EMM monitors world news in real time from the Internet and various News Agencies. The new method measures the intensity of news reporting for any country concerning a particular theme. Two normalised indicators are defined for each theme (j) and for each country (c). The first (Icj) is a measure of the relative importance for a given theme to that country. The second (Ijc) is a measure of the relative importance placed on that country with respect to the given theme by the world’s media. The method has then been applied to news articles processed by EMM for each day during August 2003. This month was characterized by a number of serious terrorist bomb attacks visible both in the EMM data and in the derived indicators. The calculated indicators for a selection of countries are presented. Their interpretation and possible biases in the data are discussed. The data are then applied to identify candidate countries for “forgotten conflicts”. These are countries with high levels of conflict but poorly reported in the world’s media.


News Article Alert System News Agency Candidate Country Thematic Indicator 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    World Event/Interaction Survey (WEIS). University of Southern California, Charles McClelland (1966-1978)Google Scholar
  2. 2.
    Integrated Data for Event Analysis (IDEA), IDEA Project (1998-2002),
  3. 3.
    Event-Based Conflict Alert/ Early Warning Tools, Delilah Al Khudhairy, Link to Report, November 11 (2002) Google Scholar
  4. 4.
    Kansas Event Data Project (KEDS), Schrodt, P.A., Dept. of Political Science, University of Kansas,
  5. 5.
    Text Analysis By Augmented Replacement (TABARI), Instructions, Schrodt, P.A., Dept. of Political Science, University of Kansas.,
  6. 6.
    Virtual Research Associates,
  7. 7.
    A Conflict-Cooperation scale for WEIS Events data. Joshua Goldstein Journal of Conflict Resolution 36, 369–385Google Scholar
  8. 8.
    Automatic Event Coding in EMM, Best, C., Coldwell, K., Horby, D., Garcia, T., Khudhairy, D.A., JRC TN (2003) Google Scholar
  9. 9.
    EMM Technical Report, Best, C., van der Goot Monica de Paola, E., Garcia, T., Horby, D., Link to Report (21/10/2002) Google Scholar
  10. 10.
    EMM - Europe Media Monitor, EC Bulletin Informatique (April 2003) Google Scholar
  11. 11.
    DARPA Translingual Information Detection, Extraction, and Summarization (TIDES) program,
  12. 12.
    James, A., Papka, R., Lavrenko, V.: On-line New Event detection and tracking. In: Proceedings of 21st Annual International ACM SIGIR Conference on R & D in Information Retrieval, Melbourne, Australia (1998)Google Scholar
  13. 13.
    Michael, S.J., Liberman, M.: Topic detection and tracking using idf-weighted Cosine Coefficient. In: DARPA Broadcast News Workshop Proceedings (1999)Google Scholar
  14. 14.
    Andrienko, G., Andrienko, N.: Interactive Maps for Visual Data Exploration. International Journal Geographic Information Science Special Issue on Visualization for exploration of Spatial Data 13(4), 355–374 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Clive Best
    • 1
  • Erik Van der Goot
    • 1
  • Monica de Paola
    • 1
  1. 1.Joint Research CentreIPSCItaly

Personalised recommendations