Understanding video propagation in online social networks

Recent statistics suggest that online social network (OSN) users regularly share video contents from video sharing sites (VSSes), and a significant amount of views of VSSes are indeed from OSN users nowadays. By crawling and comparing the statistics of same videos shared in both RenRen (the largest Facebook-like OSN in China) and Youku (the largest Youtube-like VSS in China), we find that the huge and distinguished video requests from OSNs have substantially changed the workload of VSSes. In particular, OSNs amplify the skewness of video popularity so largely that about 0.31% most popular videos account for 80% of total views. Another interesting phenomenon is that many popular videos in VSSes may not receive many requests in OSNs. To further understand these findings, we track the propagation process of videos shared in RenRen since their introduction to this OSN, and analyze the effect of potential parameters to such process, including the number of initiators (users who bring the video to the OSN directly from a VSS), branching factor (the number of users who watch the friend's shared video), and share rate (the probability that the viewers of a video will further share this video). Beyond our expectation, none of these factors determine a video's popularity in an OSN. Instead, it shows great randomness for the number of a video's potential requests when it is shared to an OSN. By modifying the basic Galton-Watson stochastic branching process, we develop a simple yet effective model to simulate the video propagation process in an OSN. Simulation results show that it can well capture the randomness of a video's popularity and the skewed video popularity distribution.

[1]  Dan Wang,et al.  Towards understanding the external links of video sharing sites: measurement and analysis , 2010, NOSSDAV '10.

[2]  Amin Vahdat,et al.  MediSyn: a synthetic streaming media service workload generator , 2003, NOSSDAV '03.

[3]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[4]  Niklas Carlsson,et al.  Characterizing and modelling popularity of user-generated videos , 2011, Perform. Evaluation.

[5]  Jiangchuan Liu,et al.  Statistics and Social Network of YouTube Videos , 2008, 2008 16th Interntional Workshop on Quality of Service.

[6]  Irving L. Traiger,et al.  Evaluation Techniques for Storage Hierarchies , 1970, IBM Syst. J..

[7]  Ben Y. Zhao,et al.  Understanding latent interactions in online social networks , 2013, TWEB.

[8]  F. Galton,et al.  On the Probability of the Extinction of Families , 1875 .

[9]  Cecilia Mascolo,et al.  Track globally, deliver locally: improving content delivery networks by tracking geographic social cascades , 2011, WWW.

[10]  Krishna P. Gummadi,et al.  Predicting emerging social conventions in online social networks , 2012, CIKM.

[11]  Zongpeng Li,et al.  Youtube traffic characterization: a view from the edge , 2007, IMC '07.

[12]  Chen Tian,et al.  Optimizing cost and performance for content multihoming , 2012, SIGCOMM '12.

[13]  Pablo Rodriguez,et al.  I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system , 2007, IMC '07.

[14]  Krishna P. Gummadi,et al.  A measurement-driven analysis of information propagation in the flickr social network , 2009, WWW '09.

[15]  Donald F. Towsley,et al.  The effect of network topology on the spread of epidemics , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[16]  Kristina Lerman,et al.  What Stops Social Epidemics? , 2011, ICWSM.

[17]  Lifeng Sun,et al.  Guiding internet-scale video service deployment using microblog-based prediction , 2012, 2012 Proceedings IEEE INFOCOM.

[18]  Tad Hogg,et al.  Using a model of social dynamics to predict popularity of news , 2010, WWW '10.

[19]  Baochun Li,et al.  Demand forecast and performance prediction in peer-assisted on-demand streaming systems , 2011, 2011 Proceedings IEEE INFOCOM.

[20]  Michael Timmers,et al.  On the Use of Reservoir Computing in Popularity Prediction , 2010, 2010 2nd International Conference on Evolving Internet.

[21]  Ibrahim Matta,et al.  Describing and forecasting video access patterns , 2011, 2011 Proceedings IEEE INFOCOM.

[22]  Jure Leskovec,et al.  Information diffusion and external influence in networks , 2012, KDD.

[23]  J. S. Maritz.  Distribution-Free Statistical Methods , 1981 .

[24]  Ben Y. Zhao,et al.  Understanding latent interactions in online social networks , 2010, IMC '10.

[25]  Haitao Li,et al.  Enhancing recommended video lists for Youtube-like social media , 2012, 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP).

[26]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[27]  Naftali Tishby,et al.  Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity , 2005, NIPS.

[28]  Matthew Rowe.  Forecasting audience increase on YouTube , 2011 .

[29]  John C. S. Lui,et al.  Mathematical Modeling of Advertisement and Influence Spread in Social Networks , 2009 .

[30]  Jon M. Kleinberg,et al.  Tracing information flow on a global scale using Internet chain-letter data , 2008, Proceedings of the National Academy of Sciences.

[31]  Lixin Gao,et al.  The impact of YouTube recommendation system on video views , 2010, IMC '10.

[32]  Krishna P. Gummadi,et al.  On word-of-mouth based discovery of the web , 2011, IMC '11.

[33]  Lada A. Adamic,et al.  The role of social networks in information diffusion , 2012, WWW.

[34]  Lifeng Sun,et al.  Peer-Assisted Social Media Streaming with Social Reciprocity , 2013, IEEE Transactions on Network and Service Management.

[35]  Lada A. Adamic,et al.  Diffusion Dynamics of Games on Online Social Networks , 2010, WOSN.

[36]  Lifeng Sun,et al.  Propagation-based social-aware replication for social video contents , 2012, ACM Multimedia.

[37]  Brian D. Davison,et al.  Predicting popular messages in Twitter , 2011, WWW.

[38]  Matthew O Jackson,et al.  Using selection bias to explain the observed structure of Internet diffusions , 2010, Proceedings of the National Academy of Sciences.

[39]  Hanghang Tong,et al.  Information spreading in context , 2011, WWW.

[40]  Bo Li,et al.  Scaling social media applications into geo-distributed clouds , 2012, 2012 Proceedings IEEE INFOCOM.

[41]  Krishna P. Gummadi,et al.  Characterizing social cascades in flickr , 2008, WOSN '08.

[42]  Yong-Yeol Ahn,et al.  Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems , 2009, IEEE/ACM Transactions on Networking.

[43]  Tad Hogg,et al.  Stochastic Models of User-Contributory Web Sites , 2009, ICWSM.

[44]  Lifeng Sun,et al.  Cloud-based social application deployment using local processing and global distribution , 2012, CoNEXT '12.

[45]  Flavio Figueiredo,et al.  The tube over time: characterizing popularity growth of youtube videos , 2011, WSDM '11.

[46]  Eric Sun,et al.  Gesundheit! Modeling Contagion through Facebook News Feed , 2009, ICWSM.

[47]  Philo van Kemenade.  Predicting the Propagation of Video Content on Twitter , 2011 .

[48]  Wolfgang Kellerer,et al.  Outtweeting the Twitterers - Predicting Information Cascades in Microblogs , 2010, WOSN.

[49]  Azer Bestavros,et al.  GISMO: a Generator of Internet Streaming Media Objects and workloads , 2001, PERV.