A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Making large-scale support vector machine learning practical
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Popularity, novelty and attention
Proceedings of the 9th ACM conference on Electronic commerce
A ranking approach to keyphrase extraction
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Detecting Macro-patterns in the European Mediasphere
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
Access: news and blog analysis for the social sciences
Proceedings of the 19th international conference on World wide web
Predicting the popularity of online content
Communications of the ACM
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We explore the problem of learning and predicting popularity of articles from online news media. The only available information we exploit is the textual content of the articles and the information whether they became popular - by users clicking on them - or not. First we show that this problem cannot be solved satisfactorily in a naive way by modelling it as a binary classification problem. Next, we cast this problem as a ranking task of pairs of popular and non-popular articles and show that this approach can reach accuracy of up to 76%. Finally we show that prediction performance can improve if more content-based features are used. For all experiments, Support Vector Machines approaches are used.