Understanding the Diffusion of YouTube Videos

  • Mattia ZeniEmail author
  • Daniele Miorandi
  • Francesco De Pellegrini
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


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.


Video Content Uniform Resource Locator Social Networking Platform Music Category Open Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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


  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. of the ACM 53(8), 80–88 (2010)CrossRefGoogle Scholar
  13. 13.
    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)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mattia Zeni
    • 1
    Email author
  • Daniele Miorandi
    • 2
  • Francesco De Pellegrini
    • 2
  1. 1.Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
  2. 2.CREATE-NETTrentoItaly

Personalised recommendations