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The paper “Learning the Importance of Latent Topics to Discover Highly Influential News Items” co-written by Krestel and Mehta has been published in KI 2010: Advances in Artificial Intelligence, the proceedings of the 33rd Annual German Conference on AI in Karlsruhe, Germany on, September 21st-24th, 2010 in Lecture Notes in Computer Science, Vol. 6359; Dillmann, R.; Beyerer, J.; Hanebeck, U.D.; Schultz, T. (Eds.)
Online news is a major source of information for many people. The overwhelming amount of new articles published every day makes it necessary to filter out unimportant ones and detect ground breaking new articles. In this paper, we propose the use of Latent Dirichlet Allocation (LDA) to find the hidden factors of important news stories. These factors are then used to train a Support Vector Machine (SVM) to classify new news items as they appear. We compare our results with SVMs based on a bag-of-words approach and other language features. The advantage of a LDA processing is not only a better accuracy in predicting important news, but also a better interpretability of the results. The latent topics show directly the important factors of a news story.