I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Description and Prediction of Slashdot Activity
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
Introduction to Information Retrieval
Introduction to Information Retrieval
Ranking Comments on the Social Web
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Power-Law Distributions in Empirical Data
SIAM Review
Using a model of social dynamics to predict popularity of news
Proceedings of the 19th international conference on World wide web
Predicting the popularity of online content
Communications of the ACM
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
An Approach to Model and Predict the Popularity of Online Contents with Explanatory Factors
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Predicting the popularity of online articles based on user comments
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
A straw shows which way the wind blows: ranking potentially popular items from early votes
Proceedings of the fifth ACM international conference on Web search and data mining
News comments: exploring, modeling, and online prediction
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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News articles are a captivating type of online content that capture a significant amount of Internet users' interest. They are particularly consumed by mobile users and extremely diffused through online social platforms. As a result, there is an increased interest in promptly identifying the articles that will receive a significant amount of user attention. This task falls under the broad scope of content popularity prediction and has direct implications in various contexts such as caching strategies or online advertisement policies. In this paper we address the problem of predicting the popularity of news articles based on user comments. We formulate the prediction task into a ranking problem where the goal is not to infer the precise attention that a content will receive but to accurately rank articles based on their predicted popularity. To this end, we analyze the ranking performance of three prediction models using a dataset of articles covering a four-year period and published by 20minutes.fr, an important French online news platform. Our results indicate that prediction methods improve the ranking performance and we observed that for our dataset a simple linear prediction method outperforms more dedicated prediction methods.