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From popularity prediction to ranking online news

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Abstract

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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Notes

  1. http://www.20minutes.fr/.

  2. http://www.telegraaf.nl/.

  3. We are aware that there are other fine-grained methods of evaluating the decay of attention over time (Lee et al. 2010; Simkin and Roychowdhury 2012; Wu et al. 2007), but for the scope of our work, this coarse characterization provides us with sufficient information.

  4. According to the latest statistics of the two sites: http://corporate.tmg.nl/en/result-second-quarter-2012 ( telegraaf ); http://www.mediametrie.fr ( 20minutes ).

  5. Statistical techniques based on maximum-likelihood methods and Kolmogorov–Smirnov statistics.

  6. The algorithm uses the number of blog posts to predict users’ interest in articles.

  7. We deploy these algorithms using RankLib open source library (Dang 2012).

  8. Information about section and author are available only for 20minutes data set.

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Acknowledgments

The work presented in this paper has been carried out at LINCS (http://www.lincs.fr) and was partially supported by the ANR project Crowd under contract ANR-08-VERS-006 and EINS, the Network of Excellence in Internet Science, FP7 grant 288021. The authors are grateful to Manos Tsagkias for sharing the telegraaf data set.

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Authors and Affiliations

  1. LIP6/CNRS, UPMC Sorbonne Universités, 4 Place Jussieu, 75005, Paris, France

    Alexandru Tatar, Marcelo Dias de Amorim & Serge Fdida

  2. Communication Systems Group, ETH Zurich, 35 Gloriastrasse, 8092, Zurich, Switzerland

    Panayotis Antoniadis

Authors
  1. Alexandru Tatar
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  2. Panayotis Antoniadis
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  3. Marcelo Dias de Amorim
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  4. Serge Fdida
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Corresponding author

Correspondence to Alexandru Tatar.

Additional information

This paper is a significant extension of our previous work, “Ranking news articles based on popularity prediction”, published at ASONAM 2012.

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Tatar, A., Antoniadis, P., Amorim, M.D.d. et al. From popularity prediction to ranking online news. Soc. Netw. Anal. Min. 4, 174 (2014). https://doi.org/10.1007/s13278-014-0174-8

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