Abstract
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 real-time 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.
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McCreadie, R., Macdonald, C., Ounis, I. (2011). A Learned Approach for Ranking News in Real-Time Using the Blogosphere. In: Grossi, R., Sebastiani, F., Silvestri, F. (eds) String Processing and Information Retrieval. SPIRE 2011. Lecture Notes in Computer Science, vol 7024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24583-1_11
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DOI: https://doi.org/10.1007/978-3-642-24583-1_11
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