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Journal Articles Social Network Analysis and Mining Year : 2014

From Popularity Prediction to Ranking Online News


News articles are an engaging type of online content that captures the attention of a significant amount of Internet users. They are particularly enjoyed by mobile users and massively spread through online social platforms. As a result, there is an increased interest in discovering the articles that will become popular among users. This objective falls under the broad scope of content popularity prediction and has direct implications in the development of new services for online advertisement and content distribution. In this paper, we address the problem of predicting the popularity of news articles based on user comments. We formulate the prediction task as 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. Using data obtained from two important news sites in France and Netherlands, we analyze the ranking effectiveness of two prediction models. Our results indicate that popularity prediction methods are adequate solutions for this ranking task and could be considered as a valuable alternative for automatic online news ranking.
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hal-00982936 , version 1 (24-04-2014)



Alexandru Tatar, Panayotis Antoniadis, Marcelo Dias de Amorim, Serge Fdida. From Popularity Prediction to Ranking Online News. Social Network Analysis and Mining, 2014, pp.4:174. ⟨10.1007/s13278-014-0174-8⟩. ⟨hal-00982936⟩
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