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 news articles reporting events that relate 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 formulate the problem of discovering such stories as Evolutionary Theme Pattern Discovery, Summary and Exploration (ETP3). We propose a method and a visualisation tool for solving ETP3 by understanding, searching and interacting with such stories and their underlying documents. 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, it relies on interactive graphs rather than natural language as the abstracted story representations. Furthermore, we present an evaluation framework. Two real-life case studies are used to illustrate and evaluate the method and tool.

Abstract. The widespread use of social media is regarded by many as the emergence of a new highway for information and news sharing promising a new information-driven "social revolution". In this paper, we analyze how this idea transfers to the news reporting domain. To analyze the role of social media in news reporting, we ask whether citizen journalists tend to create news or peddle (re-report) existing content. We introduce a framework for exploring divergence between news sources by providing multiple views on corpora in comparison. The results of our case study comparing Twitter and other news sources suggest that a major role of Twitter authors consists of neither creating nor peddling, but extending them by commenting on news.

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.

Abstract-We present the STORIES methods and tool for (a) learning an abstracted story representation from a collection of time-indexed documents; (b) visualising it in a way that encourages users to interact and explore in order to discover temporal "story stages" depending on their interests; and (c) supporting the search for documents and facts that pertain to the user-constructed story stages. In addition, we give an overview of evaluation studies of the tool.

Rich information spaces like blogs or news are full of "stories": sets of statements that evolve over time, made in fastgrowing streams of documents. Even if one reads a specific source every day and/or subscribes to a selection of feeds, one may easily lose track; in addition, it is difficult to reconstruct a story already in the past. In this paper, we present the STORIES methods and tool for (a) learning an abstracted story representation from a collection of time-indexed documents; (b) visualizing it in a way that encourages users to interact and explore in order to discover temporal "story stages" depending on their interests; (c) supporting the search for documents and facts that pertain to the user-constructed story stages; (d) discovering the most important facts in the corpora; and (e) navigating in document space along multiple meaningful dimensions of document similarity and relatedness. This combination provides users with more control, progressing from "surfing" the Web to "sailing" selected corpora of it, semantically in story space as well as between the underlying documents. An evaluation demonstrates that machine learning and interaction lead to representations that serve to retrieve coherent and relevant document subsets and that help users learn facts about the story.