document: Ranking a stream of news According to a recent survey made by Nielsen NetRatings , searching on news articles is one of the most important activity online . Indeed , Google , Yahoo , MSN and many others have proposed commercial search engines for indexing news feeds . Despite this commercial interest , no academic research has focused on ranking a stream of news articles and a set of news sources . In this paper , we introduce this problem by proposing a ranking framework which models : ( 1 ) the process of generation of a stream of news articles , ( 2 ) the news articles clustering by topics , and ( 3 ) the evolution of news story over the time . The ranking algorithm proposed ranks news information , finding the most authoritative news sources and identifying the most interesting events in the different categories to which news article belongs . All these ranking measures take in account the time and can be obtained without a predefined sliding window of observation over the stream . The complexity of our algorithm is linear in the number of pieces of news still under consideration at the time of a new posting . This allow a continuous on-line process of ranking . Our ranking framework is validated on a collection of more than 300,000 pieces of news , produced in two months by more then 2000 news sources belonging to 13 different categories ( World , U. S , Europe , Sports , Business , etc ) . This collection is extracted from the index of comeToMyHead , an academic news search engine available online .
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