On the prediction of popularity of trends and hits for user generated videos

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

Volumn , Issue , 2013, Pages 741-745

  Figueiredo, Flavio ^a  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

popularity; trends; ugc; video

Indexed keywords

CONTENT DISTRIBUTION; MEDIA PUBLISHING; ONLINE ADVERTISING; POPULARITY; RESEARCH METHODOLOGIES; STATISTICAL ERRORS; TRENDS; UGC; USER GENERATED CONTENT (UGC); USER-GENERATED VIDEO; VIDEO; VIDEO SHARING; WEB 2.0;

DATA MINING; INFORMATION RETRIEVAL; WEBSITES;

FORECASTING;

EID: 84874241714     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2433396.2433489     Document Type: Conference Paper

References (23)

1. Y. Borghol, S. Mitra, S. Ardon, N. Carlsson, D. Eager, and A. Mahanti. Characterizing and modelling popularity of user-generated videos. Performance Evaluation, 68(11):1037-1055, Nov. 2011.
2. Y. Borghol, N. S. Wales, S. Ardon, and D. Eager. The Untold Story of the Clones: Content-agnostic Factors that Impact YouTube Video Popularity. In Proc. KDD, 2012.
3. A. Brodersen, S. Scellato, and M. Wattenhofer. YouTube around the world. In Proc. WWW, Apr. 2012.
4. T. Broxton, Y. Interian, J. Vaver, and M. Wattenhofer. Catching a viral video. Journal of Intelligent Information Systems, pages 1-19, Dec. 2011.
5. M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, and S. Moon. Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems. IEEE/ACM Transactions on Networking, 17(5):1357-1370, Oct. 2009.
6. G. Cormode and B. Krishnamurthy. Key Differences between Web1.0 and Web2.0. First Monday, 13(6), 2008.
7. R. Crane and D. Sornette. Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences (PNAS), 105(41):15649-53, Oct. 2008.
8. D. Easley and J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 1 edition, July 2010.
9. F. Figueiredo, F. Benevenuto, and J. Almeida. The tube over time: characterizing popularity growth of youtube videos. In Proc. WSDM, 2011.
10. F. Figueiredo, H. Pinto, F. Belém, J. Almeida, M. Gonçalves, D. Fernandes, and E. Moura. Assessing the quality of textual features in social media. Information Processing & Management, Apr. 2012.
11. K. Filippova and K. B. Hall. Improved video categorization from text metadata and user comments. In Proc. SIGIR, 2011.
12. P. Geurts, D. Ernst, and L. Wehenkel. Extremely Randomized Trees. Machine Learning, 63(1):3-42, 2006.
13. A. Ghose and S. Yang. An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets. INFORMS: Management Science, 2010.
14. P. Gill, M. Arlitt, Z. Li, and A. Mahanti. Youtube traffic characterization: a view from the edge. In Proc. IMC, Oct. 2007.
15. A. Lacerda, M. Cristo, M. A. Gonçalves, W. Fan, N. Ziviani, and B. Ribeiro-Neto. Learning to advertise. In Proc. SIGIR, 2006.
16. K. Lerman and T. Hogg. Using a model of social dynamics to predict popularity of news. In Proc. WWW, Apr. 2010.
17. H. Pinto, J. Almeida, and M. Gonçalves. Using early view patterns to predict the popularity of YouTube videos. In Proc. WSDM, 2013.
18. G. Szabo and B. A. Huberman. Predicting the Popularity of Online Content. Communications of the ACM, 53(8):80-88, 2010.
19. Vimeo. Advertise on Vimeo. http://vimeo.com/about/advertisers, 2012.
20. M. Wattenhofer, R. Wattenhofer, and Z. Zhu. The YouTube Social Network. In Proc. ICWSM, 2012.
21. J. Yang and J. Leskovec. Patterns of temporal variation in online media. In Proc. WSDM. ACM Press, Feb. 2011.
22. 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, Feb. 2012.
23. Youtube. YouTube Press Statistics. http://www.youtube.com/t/press- statistics, 2012.