Leveraging User Intuition to Predict Item Popularity in Social Networks

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

Abstract

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.

Keywords

Social networks Web content Popularity Prediction Classification Intuition 
This is a preview of subscription content, log in to check access

References

  1. 1.
    Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53(8) (2010)Google Scholar
  2. 2.
    Pinto, H., Almeida, J.M., Gonalves, M.A.: Using early view patterns to predict the popularity of youtube videos. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, February 2013Google Scholar
  3. 3.
    Tatar, A., de Amorim, M.D., Fdida, S., Antoniadis, P.: A survey on predicting the popularity of web content. J. Internet Serv. Appl. 5(1) (2014)Google Scholar
  4. 4.
    Figueiredo, F., Almeida, J.M., Gonalves, M.A., Benevenuto, F.: On the dynamics of social media popularity: a YouTube case study. ACM Trans. Internet Technol. (TOIT) 14(4) (2014)Google Scholar
  5. 5.
    Shulman, B., Sharma, A., Cosley, D.: Predictability of popularity: gaps between prediction and understanding. In: Proceedings of the Tenth International AAAI Conference on Web and Social Media, March 2016Google Scholar
  6. 6.
  7. 7.
    Huang, J., Cheng, X.Q., Shen, H.W., Zhou, T., Jin, X.: Exploring social influence via posterior effect of word-of-mouth recommendations. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, February 2012Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

Personalised recommendations