The tube over time: Characterizing popularity growth of YouTube videos

Proceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011

Volumn , Issue , 2011, Pages 745-754

  Figueiredo, Flavio ^a   Benevenuto, Fabrício ^a   Almeida, Jussara M ^a  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

Popularity growth; Referrer; Video popularity; YouTube

Indexed keywords

CONTENT CREATORS; DATA SETS; GROWTH PATTERNS; POPULARITY GROWTH; PROPAGATION PROCESS; REFERRER; VIDEO DATASETS; VIDEO POPULARITY; VIDEO SHARING; YOUTUBE;

COPYRIGHTS; DATA MINING; GROWTH RATE; INFORMATION RETRIEVAL; INTERNET; INTERNET SERVICE PROVIDERS; SEARCH ENGINES;

WEBSITES;

EID: 79952392592     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1935826.1935925     Document Type: Conference Paper

References (21)

1. comscore:americans viewed 12 billion videos online in may 2008. http://goo.gl/2bKmP. Accessed Nov/2010.
2. New york times, uploading the avantgarde.http://goo.gl/S72M8. Accessed in Nov/2010.
3. Y.-Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong. Analysis of topological characteristics of huge online social networking services. In Proceedings of the World Wide Web Conference (WWW), 2007.
4. A. Anagnostopoulos, R. Kumar, and M. Mahdian. Influence and correlation in social networks. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2008.
5. F. Benevenuto, T. Rodrigues, V. Almeida, J. Almeida, and K. Ross. Video interactions in online video social networks. ACM Transactions on Multimedia Computing, Communications and Applications (TOMCCAP), 5 (4):1-25, 2009.
6. 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 Network (TON), 17 (5):1357-1370, 2009.
7. M. Cha, A. Mislove, and K. Gummadi. A measurement-driven analysis of information propagation in the Flickr social network. In Proceedings of the World Wide Web Conference (WWW), 2009.
8. G. Chatzopoulou, C. Sheng, and M. Faloutsos. A first step towards understanding popularity on youtube. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM), 2010.
9. R. Crane and D. Sornette. Quality, and junk videos on YouTube: Separating content from noise in an information-rich environment. In Proceedings of the AAAI Spring Symposium, 2008.
10. 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): 15649-15653, 2008.
11. P. Gill, M. Arlitt, Z. Li, and A. Mahanti. YouTube traffic characterization: A view from the edge. In Proceedings of the ACM Internet Measurement Conference (IMC), 2007.
12. M. Gonçalves, J. Almeida, L. Santos, A. Laender, and V. Almeida. On popularity in the blogosphere. IEEE Internet Computing, 14: 30-37, 2010.
13. D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2003.
14. K. Lerman and T. Hogg. Using a model of social dynamics to predict popularity of news. In Proceedings of the World Wide Web Conference (WWW), 2010.
15. J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney. Statistical properties of community structure in large social and information networks. In Proceedings of the World Wide Web Conference, 2008.
16. R. Oliveira, M. Cherubini, and N. Oliver. Looking at near-duplicate videos from a human-centric perspective. ACM Transactions on Multimedia Computing, Communications and Applications (TOMCCAP), 6 (3):1-22, 2010.
17. J. Ratkiewicz, A. Flammini, and F. Menczer. Traffic in social media I: paths through information networks. In Proc. of the Int 'I Symposium on Social Intelligence and Networking, 2010.
18. F. Suchanek, G. Kasneci, and G. Weikum. Yago: A core of semantic knowledge. In Proceedings of the World Wide Web Conference (WWW), 2007.
19. G. Szabo and B. Huberman. Predicting the popularity of online content. Communications of the ACM, 53 (8): 80-88, 2010.
20. W. Willinger, R. Rejaie, M. Torkjazi, M. Valafar, and M. Maggioni. Research on online social networks: Time to face the real challenges. SIGMETRICS Performance Evaluation Review, 37 (3): 49-54, 2009.
21. M. Zink, K. Suh, Y. Gu, and J. Kurose. Watch global, cache local: YouTube network traces at a campus network - measurements and implications. In Proceedings of the IEEE Multimedia Computing and Networking (MMCN), 2008.