Abstract
In this paper we tackle several questions arising in the context of online content diffusion. In particular, we analyse the reason why some videos become viral, how popularity of a tagged video evolves over time and if there exist recurrent patterns in the dynamics of content popularity. Indeed, while the ultimate question is if it is even possible to predict the popularity dynamics of a newly published video, several interwoven factors impact the process of diffusion of online contents. In this paper we propose a framework able to put all the previous questions into a complex system science perspective. We first analyse the mechanisms that affect the popularity growth of a tagged video. We then illustrate why a multi-scale multi-level model appears the most appropriate to capture the effect of such phenomena. We finally present an open dataset of YouTube videos’ popularity, which has been released with the aim to let researchers in the field validate their findings against real-world data.
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Notes
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The normalization over the video’s length is a limitation but is the only way to compare the curves.
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The choice of the parameter k was taken based on the analysis in [11].
References
Ahmed, M., Spagna, S., Huici, F., Niccolini, S.: A peek into the future: predicting the evolution of popularity in user generated content. In: Proceedings of the sixth ACM international conference on Web search and data mining, pp. 607–616. ACM (2013)
Altman, E., De Pellegrini, F., El Azouzi, R., Miorandi, D., Jiménez, T.: Emergence of equilibria from individual strategies in online content diffusion. In: Proceedings of IEEE INFOCOM NetSciCOmm. Turin, Italy (2013)
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.Y., Moon, S.: I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. In: Proceedings of ACM SIGCOMM IMC, pp. 1–14. ACM, New York, NY, USA (2007)
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.Y., Moon, S.: Analyzing the video popularity characteristics of large-scale user generated content systems. IEEE/ACM Trans. Networking 17(5), 1357–1370 (2009)
Chatzopoulou, G., Sheng, C., Faloutsos, M.: A first step towards understanding popularity in YouTube. In: Proceedings of IEEE INFOCOM, pp. 1–6. San Diego (2010)
Figueiredo, F., Almeida, J.M., Benevenuto, F., Gummadi, K.P.: Does content determine information popularity in social media?: a case study of YouTube videos’ content and their popularity. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pp. 979–982. ACM (2014)
Figueiredo, F., Benevenuto, F., Almeida, J.M.: The tube over time: characterizing popularity growth of YouTube videos. In: Proceedings of the ACM International Conference on Web Search and Data Mining, pp. 745–754. ACM (2011)
Li, H., Cheng, X., Liu, J.: Understanding video sharing propagation in social networks: measurement and analysis. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 10(4), 33 (2014)
Niu, G., Fan, X., Li, V., Long, Y., Xu, K.: Multi-source-driven asynchronous diffusion model for video-sharing in online social networks. IEEE Trans. Multimedia 16(7), 2025–2037 (2014)
Pinto, H., Almeida, J.M., Gonçalves, M.A.: Using early view patterns to predict the popularity of YouTube videos. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM ’13, pp. 365–374. ACM, New York, NY, USA (2013)
Richier, C., Altman, E., Elazouzi, R., Jimenez, T., Linares, G., Portilla, Y.: Bio-inspired models for characterizing youtube viewcout. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 297–305 (2014)
Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. of the ACM 53(8), 80–88 (2010)
Zhou, R., Khemmarat, S., Gao, L.: The impact of youtube recommendation system on video views. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, IMC ’10, pp. 404–410. ACM, New York, NY, USA (2010)
Acknowledgments
The work of D. Miorandi and F. De Pellegrini has been partially supported by the European Commission within the framework of the CONGAS project FP7-ICT-2011-8-317672, see www.congas-project.eu
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Zeni, M., Miorandi, D., De Pellegrini, F. (2016). Understanding the Diffusion of YouTube Videos. In: Battiston, S., De Pellegrini, F., Caldarelli, G., Merelli, E. (eds) Proceedings of ECCS 2014. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-29228-1_26
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DOI: https://doi.org/10.1007/978-3-319-29228-1_26
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