Web Mining for Understanding Stories through Graph Visualisation

Rich information spaces (like the Web or scientific publications) are full of "stories": sets of statements that evolve over time, manifested as, for example, collections of newspaper articles reporting events relating to an evolving crime investigation, sets of news articles and blog posts accompanying the development of a political election campaign, or sequences of scientific papers on a topic. In this paper, we propose a method and a visualisation tool for mapping and interacting with such stories. In contrast to existing approaches, our method concentrates on relational information and on local patterns rather than on the occurrence of individual concepts and global models. In addition, we present an evaluation framework. A real-life case study is used to illustrate and evaluate the method and tool.