Social insect inspired approach for identification and dynamic tracking of news stories on the Web

Abstract

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.

Multiple developing news stories identified and tracked by social insects and visualized using the new galactic streams and concurrent streams metaphors

“…VG 1/0752/14. This article is based on the paper [1] presented at the 2013 World Congress on Nature and Biologically Inspired Computing. We added new material on experiments and on visualization by introducing new metaphors.…”

“…Instead, each agent holds only a single term related to the stories and the final story structure is obtained through emergent behaviour of the agent swarm. This work elaborates on our previous work [1] by broadening the scope of experiments and by introducing novel means of news story visualization tailored to our approach.…”