The predictive power of online chatter
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Extracting the discussion structure in comments on news-articles
Proceedings of the 9th annual ACM international workshop on Web information and data management
Description and Prediction of Slashdot Activity
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
Can blog communication dynamics be correlated with stock market activity?
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Reading the markets: forecasting public opinion of political candidates by news analysis
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
On the relationship between novelty and popularity of user-generated content
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Linking online news and social media
Proceedings of the fourth ACM international conference on Web search and data mining
Predicting the popularity of online articles based on user comments
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
News comments: exploring, modeling, and online prediction
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Care to comment?: recommendations for commenting on news stories
Proceedings of the 21st international conference on World Wide Web
Predicting the future impact of news events
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Predicting IMDB movie ratings using social media
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Information Retrieval in the Commentsphere
ACM Transactions on Intelligent Systems and Technology (TIST)
On the Relationship between Novelty and Popularity of User-Generated Content
ACM Transactions on Intelligent Systems and Technology (TIST)
From user comments to on-line conversations
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Behaviour analysis across different types of enterprise online communities
Proceedings of the 3rd Annual ACM Web Science Conference
Predicting responses to microblog posts
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Topic-driven reader comments summarization
Proceedings of the 21st ACM international conference on Information and knowledge management
Diversifying user comments on news articles
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Characterizing and curating conversation threads: expansion, focus, volume, re-entry
Proceedings of the sixth ACM international conference on Web search and data mining
Computational perspectives on social phenomena at global scales
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
FAST: forecast and analytics of social media and traffic
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
Social reader: towards browsing the social web
Multimedia Tools and Applications
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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.