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WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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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.