Towards Characterization of Actor Evolution and Interactions in News Corpora
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.
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- 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.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.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.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.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.Silver, D., Wang, X.: Volume tracking. In: VIS 1996: 7th conference on Visualization, pp. 157–164 (1996)Google Scholar
- 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