Difference between revisions of "Catching and Forecasting Popular Videos on Youtube"
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[http://www.ida.liu.se/~nikca/papers/kdd12.pdf]Y. Borghol, S. Ardon, N. Carlsson, D. Eager, and A. Mahanti, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), Beijing, China, August 2012, to appear. | [http://www.ida.liu.se/~nikca/papers/kdd12.pdf]Y. Borghol, S. Ardon, N. Carlsson, D. Eager, and A. Mahanti, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), Beijing, China, August 2012, to appear. | ||
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+ | [http://www.hpl.hp.com/research/idl/papers/predictions/predictions.pdf]Gábor Szabó, Bernardo A. Huberman: Predicting the popularity of online content. Commun. ACM 53(8): 80-88 (2010) |
Revision as of 14:55, 6 October 2012
Teammate WANTED!
Have you ever considered why some videos could attract millions of viewS within just a few days?
Recently, research community begin to study the characteristics of the popular videos hoping the discovery would benefit the marketing and advertisement. Tom
Team
Datasets
A dataset of popular Youtube videos (1000~3000 videos) will be collected in this project. There exists a dataset of Youtube videos are avialable
- Matedata
Baseline Method
- Modeling the view growth distribution based on BA model
- Popularity prediction by Naive SVM and features.
Advantages
Challenges
Related Work
[1]Tom Broxton and Yannet Interian and Jon Vaver and Mirjam Wattenhofer: Catching a viral video. Journal of Intelligent Information Systems 2011: 1-19.
[2]Youmna Borghol, Siddharth Mitra, Sebastien Ardon, Niklas Carlsson, Derek L. Eager, Anirban Mahanti: Characterizing and modelling popularity of user-generated videos. Perform. Eval. 68(11): 1037-1055 (2011)
[3]Y. Borghol, S. Ardon, N. Carlsson, D. Eager, and A. Mahanti, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), Beijing, China, August 2012, to appear.
[4]Gábor Szabó, Bernardo A. Huberman: Predicting the popularity of online content. Commun. ACM 53(8): 80-88 (2010)