Discovering authoritative news sources and top news stories

Abstract

With the popularity of reading news online, the idea of assembling news articles from multiple news sources and digging out the most important stories has become very appealing. In this paper we present a novel algorithm to rank assembled news articles as well as news sources according to their importance and authority respectively. We employ the visual layout information of news homepages and exploit the mutual reinforcement relationship between news articles and news sources. Specifically, we propose to use a label propagation based semi-supervised learning algorithm to improve the structure of the relation graph between sources and new articles. The integration of the label propagation algorithm with the HITS like mutual reinforcing algorithm produces a quite effective ranking algorithm. We implement a system TOPSTORY which could automatically generate homepages for users to browse important news. The result of ranking a set of news collected from multiple sources over a period of half a month illustrates the effectiveness of our algorithm. © Springer-Verlag Berlin Heidelberg 2006.