Towards characterization of actor evolution and interactions in news corpora

Authors:
Rohan Choudhary;Sameep Mehta;Amitabha Bagchi;Rahul Balakrishnan
Affiliations:
Indian Institute of Technology, New Delhi, India;IBM India Research Lab, New Delhi, India;Indian Institute of Technology, New Delhi, India;Indian Institute of Technology, New Delhi, India
Venue:
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Year:
2008

Citing 5
Cited 5

Volume tracking

Proceedings of the 7th conference on Visualization '96
Event threading within news topics

Proceedings of the thirteenth ACM international conference on Information and knowledge management
Discovering evolutionary theme patterns from text: an exploration of temporal text mining

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A mixture model for contextual text mining

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
MONIC: modeling and monitoring cluster transitions

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

STORIES in Time: A Graph-Based Interface for News Tracking and Discovery

WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Connecting the dots between news articles

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Connecting Two (or Less) Dots: Discovering Structure in News Articles

ACM Transactions on Knowledge Discovery from Data (TKDD)
"Metro maps of information" by Dafna Shahaf, Carlos Guestrin and Eric Horvitz, with Ching-man Au Yeung as coordinator

ACM SIGWEB Newsletter
ESTHETE: a news browsing system to visualize the context and evolution of news stories

Proceedings of the 22nd ACM international conference on Conference on information & knowledge management

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.