Analyzing the dynamics of online video popularity

doi:10.1016/S1005-8885(17)60212-7

中国邮电高校学报(英文) ›› 2017, Vol. 24 ›› Issue (3): 58-69.doi: 10.1016/S1005-8885(17)60212-7

Analyzing the dynamics of online video popularity

欧阳书馨,李辰宇,李学明

北京邮电大学

收稿日期:2016-10-21 修回日期:2017-06-27 出版日期:2017-06-30 发布日期:2017-06-30
通讯作者: 李辰宇 E-mail:lichenyu@126.com
基金资助:
中国111项目

Analyzing the dynamics of online video popularity

Received:2016-10-21 Revised:2017-06-27 Online:2017-06-30 Published:2017-06-30
Supported by:
111 Project of China

摘要/Abstract

摘要： Given the large volume of video content and the diversity of user attention, it is of great importance to understand the characteristics of online video popularity for technological, economic and social reasons. In this paper, based on the data collected from a leading online video service provider in China, namely Youku, the dynamics of online video popularity are analyzed in-depth from four key aspects: overall popularity distribution, individual popularity distribution, popularity evolution pattern and early-future popularity relationship. How the popularity of a set of newly upload videos distributes throughout the observation period is first studied. Then the notion of active days is proposed, and the per-day and per-hour popularity distributions of individual videos are carefully studied. Next, how the popularity of an individual video evolves over time is investigated. The evolution patterns are further defined according to the number and temporal locations of popularity bursts, in order to describe the popularity growth trend. At last, the linear relationship between early video popularity and future video popularity are examined on a log-log scale. The relationship is found to be largely impacted by the popularity evolution patterns. Therefore, the specialized models are proposed to describe the correlation according to the popularity evolution patterns. Experiment results show that specialized models can better fit the correlation than a general model. Above all, the analysis results in our work can provide direct help in practical for the interested parties of online video service such as service providers, online advisers, and network operators.

关键词: online video service, online content popularity, popularity evolution pattern, early-future popularity relationship

Abstract: Given the large volume of video content and the diversity of user attention, it is of great importance to understand the characteristics of online video popularity for technological, economic and social reasons. In this paper, based on the data collected from a leading online video service provider in China, namely Youku, the dynamics of online video popularity are analyzed in-depth from four key aspects: overall popularity distribution, individual popularity distribution, popularity evolution pattern and early-future popularity relationship. How the popularity of a set of newly upload videos distributes throughout the observation period is first studied. Then the notion of active days is proposed, and the per-day and per-hour popularity distributions of individual videos are carefully studied. Next, how the popularity of an individual video evolves over time is investigated. The evolution patterns are further defined according to the number and temporal locations of popularity bursts, in order to describe the popularity growth trend. At last, the linear relationship between early video popularity and future video popularity are examined on a log-log scale. The relationship is found to be largely impacted by the popularity evolution patterns. Therefore, the specialized models are proposed to describe the correlation according to the popularity evolution patterns. Experiment results show that specialized models can better fit the correlation than a general model. Above all, the analysis results in our work can provide direct help in practical for the interested parties of online video service such as service providers, online advisers, and network operators.

Key words: online video service, online content popularity, popularity evolution pattern, early-future popularity relationship

参考文献

1. Cisco visual networking index: forecast and methodology, 2016-2021-2019. San Jose, CA, USA: Cisco

2. Szabo G, Huberman B A. Predicting the popularity of online content. Communications of the ACM, 2010, 53(8): 80-88

3. Crane R, Sornette D. Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences, 2008, 105(41): 15649-15653

4. Chatzopoulou G, Sheng C, Faloutsos M. A first step towards understanding popularity in YouTube. Proceedings of the 2010 IEEE Conference on Computer Communications Workshops, Mar 15-19, 2010, San Diego, CA, USA. Piscataway, NJ, USA: IEEE, 2010: 6p

5. Gill P, Arlitt M, Li Z P, et al. YouTube traffic characterization: a view from the edge. Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement (IMC’07), Oct 24-26, 2007, San Diego, CA, USA. New York, NY, USA: ACM, 2007: 15-28

6. Zink M, Suh K, Gu Y, et al. Characteristics of YouTube network traffic at a campus network–measurements, models, and implications. Computer Networks: The International Journal of Computer and Telecommunications Networking, 2009, 53(4): 501-514

7. Cha M, Kwak H, Rodriguez P, et al. Analyzing the video popularity characteristics of large-scale user generated content systems. IEEE/ACM Transactions on Networking, 2009, 17(5): 1357-1370

8. Tan X Y, Guo Y C, Chen Y S, et al. Characterizing user popularity preference in a large-scale online video streaming system. Proceedings of the 6th International Conference on Wireless, Mobile and Multi-Media, Nov 20-23, 2015, Beijing, China. Piscataway, NJ, USA: IEEE, 2015: 246-249

9. Liu W, Zhang G, Chen J, et al. A measurement-based study on application popularity in android and iOS APP stores. Proceedings of the 2015 Workshop on Mobile Big Data, Jun 22-25, 2015, Hangzhou, China. New York, NY, USA: ACM, 2015: 13-18

10. Li C Y, Liu J. Large-scale characterization of comprehensive online video service in mobile network. Proceedings of the 2016 IEEE International Conference on Communications (ICC’16), May 22-7, 2016, Kuala Lumpur, Malaysia. Piscataway, NJ, USA: IEEE, 2016: 7p

11. Cheng X, Dale C, Liu J C. Statistics and social network of YouTube videos. Proceedings of the 16th International Workshop on Quality of Service (IWQoS’08), Jun 2-4, 2008, Enschede, Netherlands. Piscataway, NJ, USA: IEEE, 2008: 229-238.

12. Figueiredo F, Benevenuto F, Almeida J M. The tube over time: characterizing popularity growth of YouTube videos. Proceedings of the 2011 ACM International Conference on Web Search and Data Mining (WSDM’11), Feb 9-12, 2011, Hong Kong, China. New York, NY, USA: ACM, 2008: 745-754

13. Figueiredo F, Almeida J M, Gonçalves M A, et al. On the dynamics of social media popularity: a YouTube case study. ACM Transactions on Internet Technology, 2014, 14(4): Article 24

14. Gonçalves G D, Figueiredo F, Almeida J M, et al. Characterizing scholar popularity: a case study in the computer science research community. Proceedings of the 2014 IEEE/ACM Joint Conference on Digital Libraries, Sept 8-14, 2014, London, UK. Piscataway, NJ, USA: IEEE, 2014: 57-66

15. Yang J, Leskovec J. Patterns of temporal variation in online media. Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM’11), Feb 9-12, 2011, Hong Kong, China. New York, NY, USA: ACM, 2011: 177-186

16. Borghol Y, Mitra S, Ardon S, et al. Characterizing and modelling popularity of user-generated videos. Performance Evaluation, 2011, 68(11): 1037-1055

17. Wu F, Huberman BA. Novelty and collective attention. Proceedings of the National Academy of Sciences, 2007, 104(45): 17599-17601

18. Ratkiewicz J, Fortunato S, Flammini A, et al. Characterizing and modeling the dynamics of online popularity. Physical Review Letters, 2010, 105(15): Article 158701

19. Cheng X, Liu J C, Dale C. Understanding the characteristics of internet short video sharing: a YouTube-based measurement study. IEEE Transactions on Multimedia, 2013, 15(5): 1184-1194

20. Roy S D, Mei T, Zeng W J, et al. Towards cross-domain learning for social video popularity prediction. IEEE Transactions on Multimedia, 2013, 15(6): 1255-1267

21. Vallet D, Berkovsky S, Ardon S, et al. Characterizing and predicting viral-and-popular video content. Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM’15), Oct 18-23, 2015, Melbourne, Australia. New York, NY, USA: ACM, 2015: 1591-1600

22. Pinto H, Almeida J M, Gonçalves M A. Using early view patterns to predict the popularity of YouTube videos. Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM’13), Feb 4-8, 2013, Rome, Italy. New York, NY, USA: ACM, 2013: 365-374

23. Tatar A, Leguay J, Antoniadis P, et al. Predicting the popularity of online articles based on user comments. Proceedings of the 2011 International Conference on Web Intelligence, Mining and Semantics (WIMS’11), May 25-27, 2011, Sogndal, Norway. New York, NY, USA: ACM, 2011: Article 67

24. Chen H Q, Zhong X F, Sun J, et al. Online prediction algorithm of the news’ popularity for wireless cellular pushing. Proceedings of the 2015 IEEE/CIC International Conference on Communications in China (ICCC’15), Nov 2-4, 2015, Shenzhen, China. Piscataway, NJ, USA: IEEE, 2015: 5p

25. Tatar A, Antoniadis P, De Amorim M D, et al. From popularity prediction to ranking online news. Social Network Analysis and Mining, 2014, 4: Article 174

26. Lin Y J, Yeh M Y, Chiu F Y, et al. Predicting popularity of articles on bulletin board system. Proceedings of the 2016 International Conference on Big Data and Smart Computing (BigComp’16), Jan 18-20, 2016, Hong Kong, China. Piscataway, NJ, USA: IEEE, 2016: 169-176

27. Ma C S, Yan Z S, Chen C W. Forecasting initial popularity of just-uploaded user-generated videos. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP’16), Sept 25-28, 2016, Phoenix, AZ, USA. Piscataway, NJ, USA: IEEE, 2016: 474-478

28. Kong Q C, Mao W J, Liu C Y. Popularity prediction based on interactions of online contents. Proceedings of the 4th International Conference on Cloud Computing and Intelligence Systems (CCIS’16), Aug 17-19, 2016, Beijing, China. Piscataway, NJ, USA: IEEE, 2016: 5p

29. Lemahieu R, Van Canneyt S, De Boom C, et al. Optimizing the popularity of twitter messages through user categories. Proceedings of the 2015 IEEE International Conference on Data Mining Workshop (ICDMW’15), Nov 14-17, 2015, Atlantic City, NJ, USA. Piscataway, NJ, USA: IEEE, 2015: 1396-1401

Analyzing the dynamics of online video popularity

Analyzing the dynamics of online video popularity

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