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
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Using data collections available on the Internet has for many people became the main medium for staying informed about the world. Many of these collections are in nature dynamic, evolving as the subjects they describe change. The goal of different research areas is to identify and highlight these changes to better enable readers to track stories. In this work we restrict ourselves to news collections and investigate "real-life" effectiveness and usability of temporal text mining (TTM) story tracking methods. We propose a new story tracking method and build a tool to support it. Additionally, we investigate the effectiveness and usability of story tracking methods and define a new frameworks for automatic and user oriented evaluation. We built methods and tools which allow for understanding, discovery, and search through user interaction. Although there are many TTM methods developed there is a lack of common evaluation procedure. Therefore, we propose an evaluation framework for measuring how different TTM methods discover novel "facts". Apart from the automatic evaluation we are interested in how can users interact with pattens and learn about the underlying subjects of the story they track. For this purpose we propose a user testing environment that measures speed and accuracy in which users can use story tracking methods to discover predefined sets of ground-truth sentences.