Ilija Subasic

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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(More)
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(More)
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(More)
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(More)
A query burst is a period of heightened interest of users on a topic which yields a higher frequency of the search queries related to it. In this paper we examine the behavior of search engine users during a query burst, compared to before and after this period. The purpose of this study is to get insights about how search engines and content providers(More)
Many document collections are by nature dynamic, evolving as the topics or events they describe change. The goal of temporal text mining is to discover bursty patterns and to identify and highlight these changes to better enable readers to track stories. Here, we focus on the news domain, where the changes revolve around novel, previously unpublished, "(More)
Using data collections available on the Internet has for many people become the main medium for staying informed about the world. Many of these collections are dynamic by nature, evolving as the subjects they describe change. We present the STORIES system for (a) learning an abstracted story representation from a collection of time-indexed documents ; (b)(More)
Abstr act. The Internet has become for many the most important medium for staying informed about current news events. Some events cause heightened interest on a topic, which in turn yields a higher frequency of the search queries related to it. These queries are going through a " query burst ". In this paper we examine the behavior of search engine users(More)
With the growing number of document sets accessible online, tracking their evolution over time (story tracking) became an increasingly interesting problem. In this paper we propose a story tracking method based on the dynamics of keyword-association graphs. We create a graph representation of the story evolution that we call story graphs, and investigate(More)