Predicting the volume of comments on online news stories

Authors
Publication date 2009
Host editors
  • D. Cheung
  • I.-Y. Song
  • W. Chu
  • X. Hu
  • J. Lin
  • J. Li
  • Z. Peng
Book title Proceedings of the 18th ACM Conference on Information and Knowledge Management, Hong Kong
ISBN
  • 9781605585123
Event 18th ACM Conference on Information and Knowledge Management (CIKM 2009), Hong Kong, China
Pages (from-to) 1765-1768
Publisher New York: ACM
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract
On-line news agents provide commenting facilities for readers to express their views with regard to news stories. The number of user supplied comments on a news article may be indicative of its importance or impact. We report on exploratory work that predicts the comment volume of news articles prior to publication using five feature sets. We address the prediction task as a two stage classification task: a binary classification identifies articles with the potential to receive comments, and a second binary classification receives the output from the first step to label articles "low" or "high" comment volume. The results show solid performance for the former task, while performance degrades for the latter.
Document type Conference contribution
Language English
Published at http://doi.acm.org/10.1145/1645953.1646225
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