Predicting the Volume of Comments on Online News Stories
Manos Tsagias, Maarten de Rijke and Wouter Weerkamp
In: ACM 18th Conference on Information and Knowledge Managment (CIKM 2009), 2-6 Nov 2009, Hong Kong.
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.