Predicting the popularity of online content

Communications of the ACM

Volumn 53, Issue 8, 2010, Pages 80-88

  Szabo, Gabor ^a   Huberman, Bernardo A ^a  

^a HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77955233534     PISSN: 00010782     EISSN: 15577317     Source Type: Journal    
DOI: 10.1145/1787234.1787254     Document Type: Article

References (10)

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3. Cheng, X., Dale, C., and Liu, J. Statistics and social network of YouTube videos. In Proceedings of the 16th International Workshop on Quality of Service (Enschede, the Netherlands, June 2-4, 2008), 229-238.
4. Digg API; http://digg.com/api/docs/overview
5. Feller. W. An Introduction to Probability Theory and Its Applications, Vol. 1. John Wiley & sons, Inc., New York, 1968.
6. Gill, P., Arlitt, M., Li, Z., and Mahanti, A. YouTube traffic characterization: A view from the edge. In Proceedings of the Seventh ACM SIGCOMM Conference on Internet Measurement (San Diego, Oct. 24-26). ACM Press, New York, 2007, 15-28.
7. Lerman, K. Social information processing in news aggregation. IEEE Internet Computing (Special Issue on Social Search) 11, 6 (Nov. 2007), 16-28.
8. Richardson, M., Dominowska, E., and Ragno, R. Predicting clicks: Estimating the click-through rate for new ads. In Proceedings of the 16th International Conference on the World Wide Web (banff, alberta, Canada, May 8-12). ACM Press, New York, 2007, 521-530.
9. Wu, F. and Huberman, B.A. Novelty and collective attention. Proceedings of the National Academy of Sciences 104, 45 (nov. 2007).
10. YouTube API; http://code.google.com/apis/youtube/overview.html