Aining Wang
ABSTRACT
With the rise of mobile video streaming service, video popularity has drawn a great deal of attention in both academia and streaming industry. In this paper, we present a novel view of video popularity as time series. We collect more than two billion view records from one of the largest mobile streaming content providers in China. Using Discrete Fourier transform, we decompose popularity series of videos into two parts, the seasonal and residual component series. We find that the traffic dynamics of videos is governed by a small number of components in the frequency spectrum, and that traffic patterns within and across days differ from type to type. We further investigate the short-term and long-term stability of video popularity, and find that the stability metrics are highly correlated with total video requests.
- H. Yu, D. Zheng, B. Y. Zhao, and W. Zheng. Understanding user behavior in large-scale video-on-demand systems. In Proc. of ACM EuroSys'06, pp. 333--344, Leuven, Belgium, 2006. Google Scholar
Digital Library
- 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. of ACM IMC'07, pp. 1--14. San Diego, CA, USA, 2007. Google ScholarDigital Library
- G. Chatzopoulou, C. Sheng, and M. Faloutsos. A first step towards understanding popularity in youtube. In INFOCOM IEEE Conference on Computer Communications Workshops, 2010, pp. 1--6. San Diego, CA, USA, 2010.Google ScholarCross Ref
- H. Yu, L. Xie, and S. Sanner. Twitter-driven youtube views: Beyond individual influencers. In Proc. of ACM MM'14, pp. 869--872. Orlando, FL, USA, 2014. Google ScholarDigital Library
- H. Li, X. Ma, F. Wang, J. Liu, and K. Xu. On popularity prediction of videos shared in online social networks. In Proc. of ACM CIKM'13, pp. 169--178. San Francisco, CA, USA, 2013. Google ScholarDigital Library
- H. Abrahamsson and M. Nordmark. Program popularity and viewer behaviour in a large tv-on-demand system. In Proc. of ACM IMC'12, pp. 199--210. Boston, MA, USA, 2012. Google ScholarDigital Library
- K.-I. Goh and A.-L. Barabási. Burstiness and memory in complex systems. EPL, 81(4):48002, 2008.Google ScholarCross Ref
- B. Cici, M. Gjoka, A. Markopoulou, and C. T. Butts On the decomposition of cell phone activity patterns and their connection with urban ecology. In Proc. ACM MobiHoc'15, pp. 317--326. Hangzhou, China, 2015. Google ScholarDigital Library
Index Terms
- A first view on mobile video popularity as time series
Recommendations
Predicting the popularity of viral topics based on time series forecasting
Thanks to social media platforms, topics are diffused online. The ones which diffuse rapidly and widely are known as viral topics. Predicting the popularity of viral topics is important to online recommendation systems and marketing services. However, ...
Predicting Information Popularity: A Study of Sina Weibo
ICCIS 2017: Proceedings of the 2017 2nd International Conference on Communication and Information SystemsIn this paper, we explore if information characteristics, including accuracy, familiarity, fluency, importance, informativeness and relevance, can predict the popularity of a message posted and reposted on Sina Weibo, the most popular social media ...
An analysis of influential users for predicting the popularity of news tweets
PRICAI'16: Proceedings of the 14th Pacific Rim International Conference on Trends in Artificial IntelligenceTwitter plays an important role in today social network. Its key mechanism is retweet that disseminates information to broad audiences within a very short time and help increases the popularity of the social content. Therefore, an effective model for ...
Comments