C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
WWW '05 Proceedings of the 14th international conference on World Wide Web
Independence and commitment: assumptions for rapid training and execution of rule-based POS taggers
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Discovering authoritative news sources and top news stories
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Ranking web news via homepage visual layout and cross-site voting
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Learning the importance of latent topics to discover highly influential news items
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Predicting the future impact of news events
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
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In this age of awareness, people have access to information like never before. Hundreds of newspapers and millions of bloggers present news and their interpretations in an openly accessible manner. With globalization, distant events can have impact on people thousands of miles away. While expert humans can recognize a potentially important piece of news, this is still a difficult problem for an automatic system. Since people are increasingly relying on multiple online sources of information, it is important to support users in filtering news automatically. In this work, we consider the problem of anticipating news story importance, i.e. given a news item, predicting if it will be of interest for a majority of users. Such ranking is currently done manually for newspapers, and we explore automatic approaches and indicative features for the same. Our main conclusion is that importance prediction is a hard problem, and pure textual features are not sufficient for classifiers with 90% accuracy.