In this paper we present an approach, inspired by honey bees, that allows us to take a glance at current events by exploring a portion of the Web and extracting keywords, relevant to current news stories. Not unlike the bees, that cooperate together to retrieve little bits of food, our approach uses agents to select random keywords and carry them from one article to another, landing only on the articles relevant to the keyword. Keywords that best represent multiple articles are selected, while keywords not relevant to articles are subsequently discarded and not explored further. Our results show, that with this approach, it is possible to extract keywords relevant to news stories, without utilizing learning methods, or analysis of a data corpus.

In this paper we present an approach to identifica tion and tracking of currently unfolding news stories extracted from the news articles published on the Web. Our approach utilizes a set of social insect inspired agents to retrieve articles from the Web and identify popular terms, referred to as story words, relevant to the ongoing news stories. This allows for a dy namic approach that reflects the changes in article space as new stories unfold and new articles are added. Subsequently a graph representation of the article space is constructed, that contains retrieved articles and identified story words interconnected by edges according to relationships of relevance identified between elements of the graph. Stories are then extracted from the constructed graph by using Louvain method, commonly used to identify communities within modular graphs. Using this approach we have been able to identify news stories in a stream of articles retrieved from the Web with precision of 75.56 %, with best precision generally achieved for recent news stories described by popular story words.

We have developed an approach to identification and tracking of currently unfolding news stories extracted from the news articles published on the Web. Our approach employs a set of agents to retrieve those articles from the Web that might refer to some developing news story. The set of agents is inspired by social insects, in particular by a bee colony. Bees identify popular terms, referred to as story words, relevant to the ongoing news stories and use them in foraging articles. This allows for a dynamic approach that reflects the changes in article space as new stories unfold and new articles are added. Subsequently a graph representation of the article space is constructed that contains retrieved articles and identified story words interconnected by edges according to relationships of relevance identified between elements of the graph. Stories are then extracted from the constructed graph by using Louvain method, commonly used to identify communities within modular graphs. Using this approach we have been able to identify news stories in a stream of articles retrieved from the Web with precision of 75.56%, with best precision generally achieved for recent news stories described by popular story words. Further we developed ways of visualization of multiple stories represented by sets of articles ordered in time. We propose two new metaphors both employing an exponential timeline. Both galactic streams and concurrent streams are highly suitable for visualizing multiple developing stories.

No abstract