Using early view patterns to predict the popularity of YouTube videos

WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining

Volumn , Issue , 2013, Pages 365-374

  Pinto, Henrique ^a   Almeida, Jussara M ^a   Gonçalves, Marcos A ^a  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

popularity prediction; regression models; youtube

Indexed keywords

BASELINE MODELS; DESIGN AND EVALUATIONS; HISTORICAL INFORMATION; POPULARITY PREDICTIONS; REGRESSION MODEL; SQUARED ERRORS; TARGETED ADVERTISING; WEB CONTENT; YOUTUBE;

DATA MINING; FORECASTING; INFORMATION RETRIEVAL; REGRESSION ANALYSIS;

WEBSITES;

EID: 84874243168     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2433396.2433443     Document Type: Conference Paper

References (18)

1. Alexa.com. Alexa top 500 global sites. http://www.alexa.com/topsites, 2011. [Online; acessed 2-November-2011].
2. S. Boyd and L. Vandenberghe. Convex optimization. Cambridge University Press, 2004.
3. M. Cha, H. Kwak, P. Rodriguez, Y. Ahn, and S. Moon. Analyzing the video popularity characteristics of large-scale user generated content systems. ACM Trans. on Networking, 17(5), 2009.
4. M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, and S. Moon. I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system. In Proc. ACM Internet Measurement Conference, 2007.
5. R. Crane and D. Sornette. Robust dynamic classes revealed by measuring the response function of a social system. Proc. National Academy of Sciences, 105(41), 2008.
6. F. Figueiredo, F. Benevenuto, and J. Almeida. The tube over time: Characterizing popularity growth of youtube videos. In Proc. Conference of Web Search and Data Mining, 2011.
7. J. Friedman, T. Hastie, and R. Tibshirani. The elements of statistical learning, volume 1. Springer Series in Statistics, 2001.
8. M. Gonçalves, J. Almeida, L. Santos, A. Laender, and V. Almeida. On popularity in the blogosphere. Internet Computing, 14(3), 2010.
9. V. Heffernan. Uploading the avant-garde. http://www.nytimes.com/2009/09/ 06/magazine/06FOB-medium-t.html, 2009. [Online; acessed 2-November-2011].
10. R. Jain. The art of computer systems performance analysis. John Wiley & Sons, 2008.
11. J. Lee, S. Moon, and K. Salamatian. An approach to model and predict the popularity of online contents with explanatory factors. In Int'l. Conf. on Web Intelligence and Intelligent Agent Technology, 2010.
12. K. Lerman and T. Hogg. Using a model of social dynamics to predict popularity of news. In Proc. World Wide Web Conference. ACM, 2010.
13. T. Rodrigues, F. Benevenuto, V. Almeida, J. Almeida, and M. Gonçalves. Equal but different: A contextual analysis of duplicated videos on youtube. Springer Journal of the Brazilian Computer Society, 16(3), 2010.
14. F. Suchanek, G. Kasneci, and G. Weikum. Yago: a core of semantic knowledge. In Proc. World Wide Web Conference, 2007.
15. G. Szabo and B. Huberman. Predicting the popularity of online content. Communic. of ACM, 53(8), 2010.
16. F. Wu and B. Huberman. Novelty and collective attention. Proc. National Academy of Sciences, 104(45), 2007.
17. T. Wu, M. Timmers, D. De Vleeschauwer, and W. Van Leekwijck. On the use of reservoir computing in popularity prediction. In Proc. Conference on Evolving Internet, 2010.
18. P. Yin, P. Luo, M. Wang, and W.-C. Lee. A straw shows which way the wind blows: Ranking potentially popular items from early votes. In Proc. WSDM 2012, 2012.