Newspaper websites and news aggregators rank news stories
by their newsworthiness in real-time for display to the user. Recent work
has shown that news stories can be ranked automatically in a retrospective
manner based upon related discussion within the blogosphere. However,
it is as yet undetermined whether blogs are sufficiently fresh to rank
stories in real-time. In this paper, we propose a novel learning to rank
framework which leverages current blog posts to rank news stories in a
real-time manner. We evaluate our proposed learning framework within
the context of the TREC Blog track top stories identification task. Our
results show that, indeed, the blogosphere can be leveraged for the realtime
ranking of news, including for unpredictable events. Our approach
improves upon state-of-the-art story ranking approaches, outperforming
both the best TREC 2009/2010 systems and its single best performing
feature.
Richard McCreadie, Craig Macdonald and Iadh Ounis.
A Learned Approach for Ranking News in Real-time using the Blogosphere.
In Proceedings of SPIRE 2011
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