Leveraging User Intuition to Predict Item Popularity in Social Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10542)


We investigate the problem of early prediction of item popularity in online social networks. Prior work claims that the time taken by each item to reach i adopters (i being a small number around 5) has a higher predictive power than other non-temporal features, such as those related to the characteristics of the adopters. Here, we challenge this claim by proposing a new feature, based on the users’ intuitions, which is shown to provide significantly better predictive power for the most popular items than the above-mentioned temporal feature. A GoodReads dataset is used to illustrate the merits of the proposed method.


Social networks Web content Popularity Prediction Classification Intuition 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.FIL, TICLabUniversité Internationale de RabatRabatMorocco
  2. 2.School of EEEUniversity of LeedsLeedsUK

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