A Statistical Approach to the Discovery of Ephemeral Associations among News Topics*

  • M. Montes-y-Gómez
  • A. Gelbukh
  • A. López-López
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2113)


News reports are an important source of information about society. Their analysis allows understanding its current interests and measuring the social importance and influence of different events. In this paper, we use the analysis of news as a means to explore the society interests. We focus on the study of a very common phenomenon of news: the influence of the peak news topics on other current news topics. We propose a simple, statistical text mining method to analyze such influences. We differentiate between the observable associations— those discovered from the newspapers—and the real-world associations, and propose a technique in which the real ones can be inferred from the observable ones. We illustrate the method with some results obtained from preliminary experiments and argue that the discovery of the ephemeral associations can be translated into knowledge about interests of society and social behavior.


Normalization Effect News Report Observable Frequency Society Interest News Topic 
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 2001

Authors and Affiliations

  • M. Montes-y-Gómez
    • 1
  • A. Gelbukh
    • 1
  • A. López-López
    • 2
  1. 1.Center for Computing Research (CIC), National Polytechnic Institute (IPN)Mexico
  2. 2.Instituto Nacional de Astrofísica, Optica y Electrónica (INAOE)PueblaMexico

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