Towards Characterization of Actor Evolution and Interactions in News Corpora

  • Rohan Choudhary
  • Sameep Mehta
  • Amitabha Bagchi
  • Rahul Balakrishnan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4956)


The natural way to model a news corpus is as a directed graph where stories are linked to one another through a variety of relationships. We formalize this notion by viewing each news story as a set of actors, and by viewing links between stories as transformations these actors go through. We propose and model a simple and comprehensive set of transformations: create, merge, split, continue, and cease. These transformations capture evolution of a single actor and interactions among multiple actors. We present algorithms to rank each transformation and show how ranking helps us to infer important relationships between actors and stories in a corpus. We demonstrate the effectiveness of our notions by experimenting on large news corpora.


News Story Interaction Graph News Item Topic Detection News Corpus 
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.
    Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study: Final report. In: DARPA Broadcast News Transcription and Understanding Workshop, pp. 194–218 (2006)Google Scholar
  2. 2.
    Choudhary, R., Mehta, S., Bagchi, A., Balakrishna, R.: A framework for exploring news corpora by actor evolution and interaction. IBM Research Report- RI07004 (2007)Google Scholar
  3. 3.
    Mei, Q., Zhai, C.: Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In: KDD 2005: 11th ACM SIGKDD international conference on Knowledge Discovery and data mining, pp. 198–207 (2005)Google Scholar
  4. 4.
    Mei, Q., Zhai, C.: A mixture model for contextual text mining. In: KDD 2006: 12th ACM SIGKDD international conference on Knowledge Discovery and data mining, pp. 649–655 (2006)Google Scholar
  5. 5.
    Nallapati, R., Feng, A., Peng, F., Allan, J.: Event threading within news topics. In: CIKM 2004: 13th ACM International Conference on Information and Knowledge Management, pp. 446–453 (2004)Google Scholar
  6. 6.
    Silver, D., Wang, X.: Volume tracking. In: VIS 1996: 7th conference on Visualization, pp. 157–164 (1996)Google Scholar
  7. 7.
    Spiliopoulou, M., Ntoutsi, I., Theodoridis, Y., Schult, R.: Monic: modeling and monitoring cluster transitions. In: KDD 2006: 12th ACM SIGKDD international conference on Knowledge Discovery and data mining, pp. 706–711 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rohan Choudhary
    • 1
  • Sameep Mehta
    • 2
  • Amitabha Bagchi
    • 1
  • Rahul Balakrishnan
    • 1
  1. 1.Indian Institute of TechnologyNew DelhiIndia
  2. 2.IBM India Research LabNew DelhiIndia

Personalised recommendations