Tuesday, 20 November 2012

News Article Ranking: Leveraging the Wisdom of Bloggers

Every day, editors rank news articles for placement within their
newspapers. In this paper, we investigate how news article ranking
can be performed automatically. In particular, we investigate
the blogosphere as a prime source of evidence, on the intuition that
bloggers, and by extension their blog posts, can indicate interest
in one news article or another. Moreover, we propose to model this
automatic news article ranking task as a voting process, where each
relevant blog post acts as a vote for one or more news articles. We
evaluate this approach using the TREC 2009 Blog track top news
story identification task judgments, showing strong performance in
comparison to TREC systems, as well as two alternative baseline
rankings. Furthermore, to increase the accuracy of the proposed approach,
we examine temporal re-ranking techniques, corpus cleaning
of inappropriate articles and article expansion to counter vocabulary
mismatch. We conclude that, overall, blog post evidence can
be a useful indicator to a news editor as to the importance of various
news stories, and that our approaches for extracting this evidence
are effective.

Richard McCreadie, Craig Macdonald, and Iadh Ounis.
News Article Ranking: Leveraging the Wisdom of Bloggers.
In Proceedings of RIAO 2010.
Paris, France, 2010.



Post a Comment

newer post older post Home