Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Call Me Guru: User Categories and Large-Scale Behavior in YouTube

  • Chapter
Social Media Modeling and Computing

  • 1163 Accesses

Abstract

While existing studies on YouTube’s massive user-generated video content have mostly focused on the analysis of videos, their characteristics, and network properties, little attention has been paid to the analysis of users’ long-term behavior as it relates to the roles they self-define and (explicitly or not) play in the site. In this chapter, we present a statistical analysis of aggregated user behavior in YouTube from the perspective of user categories, a feature that allows people to ascribe to popular roles and to potentially reach certain communities. Using a sample of 270,000 users, we found that a high level of interaction and participation is concentrated on a relatively small, yet significant, group of users, following recognizable patterns of personal and social involvement. Based on our analysis, we also show that by using simple behavioral features from user profiles, people can be automatically classified according to their category with accuracy rates of up to 73%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The two-sample KS test is a non-parametric method which is sensitive to differences in both location and shape of the empirical cumulative distribution functions (CDFs) of two samples, and makes no assumption about the distribution of data. The null hypothesis of this statistic is that the samples are drawn from the same distribution. Thus, a KS test that yields a p-value less than a specified α, leads to the rejection of the null hypothesis, and favors the hypothesis that distributions are different [6].

  2. 2.

    The two-proportion z-test is used to compare proportions of two independent binomial samples. The null hypothesis of this statistic is that the two proportions are equal. Thus, a two-proportion z-test giving a p-value less than a specific α (typically 0.05), leads to the rejection of the null hypothesis, and indicates that the proportions are different [18].

References

  1. Benevenuto, F., Duarte, F., Rodrigues, T., Almeida, V.A.F., Almeida, J.M., Ross, K.W.: Understanding video interactions in YouTube. In: MM ’08: Proceeding of the 16th ACM International Conference on Multimedia, pp. 761–764. ACM, New York (2008)

    Chapter  Google Scholar 

  2. Biel, J.-I., Gatica-Perez, D.: Wearing a YouTube hat: directors, comedians, gurus, and user aggregated behavior. In: MM ’09: Proceedings of the Seventeen ACM International Conference on Multimedia, pp. 833–836 (2009)

    Chapter  Google Scholar 

  3. Burgess, J., Green, J.: YouTube: Online Video and Participatory Culture. Polity, Cambridge (2009)

    Google Scholar 

  4. 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: IMC ’07: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 1–14. ACM, New York (2007)

    Chapter  Google Scholar 

  5. Cheng, X., Dale, C., Liu, J.: Statistics and social network of YouTube videos. In: Quality of Service, 2008. IWQoS 2008. 16th International Workshop on, pp. 229–238 (2008)

    Google Scholar 

  6. Conover, W.J.: Practical Nonparametric Statistics. Wiley, New York (1971)

    Google Scholar 

  7. Gill, P., Arlitt, M., Li, Z., Mahanti, A.: YouTube traffic characterization: a view from the edge. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 15–28 (2007)

    Chapter  Google Scholar 

  8. Griffith, M.: Looking for you: An analysis of video blogs. In: Annual Meeting of the Association for Education in Journalism and Mass Communication (2007)

    Google Scholar 

  9. Halvey, M., Keane, M.T.: Exploring social dynamics in online media sharing. In: Proc. of the 16th International Conference on World Wide Web, pp. 1273–1274 (2007)

    Chapter  Google Scholar 

  10. Kruitbosch, G., Nack, F.: Broadcast yourself on YouTube—really? In: Proceedings of the 3rd ACM International Workshop on Human-Centered Computing, pp. 7–10 (2008)

    Chapter  Google Scholar 

  11. Landry, B.M., Guzdial, M.: Art or circus? characterizing user-created video on YouTube. Technical report, Georgia Institute of Technology (2008)

    Google Scholar 

  12. Lange, P.G.: Commenting on comments: investigating responses to antagonism on YouTube. In: Conference on Society for Applied Anthropology (2007)

    Google Scholar 

  13. Lange, P.G.: Publicly private and privately public: social networking on YouTube. J. Comput. Mediat. Commun. 1(13) (2007)

    Google Scholar 

  14. Lin, W.-H., Hauptmann, A.: Identifying ideological perspectives of web videos using folksonomies. In: AAAI Fall Symposium on Multimedia Information Extraction (2008)

    Google Scholar 

  15. Maia, M., Almeida, J., Almeida, V.: Identifying user behavior in online social networks. In: SocialNets ’08: Proceedings of the 1st Workshop on Social Network Systems, pp. 1–6. ACM, New York (2008)

    Chapter  Google Scholar 

  16. Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42 (2007)

    Chapter  Google Scholar 

  17. Molyneaux, H., O’Donnell, S., Gibson, K., Singer, J.: Exploring the gender divide on YouTube: An analysis of the creation and reception of vlogs. Am. Commun. J. 10(2) (2008)

    Google Scholar 

  18. Newcombe, R.G.: Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat. Med. 8(17), 857–872 (1998)

    Article  Google Scholar 

  19. O’Donnell, S., Gibson, K., Milliken, M., Singer, J.: Reacting to YouTube videos: Exploring differences among user groups. In: Proceedings of the International Communication Association Annual Conference, pp. 22–26 (2008)

    Google Scholar 

  20. Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53(8), 80–88 (2010)

    Article  Google Scholar 

  21. Website Monitoring Blog. YouTube Facts & Figures (history & Statistics). http://www.website-monitoring.com/blog/2010/05/17/youtube-facts-and-figures-history-statistics/

  22. YouTube Fact Sheet. http://www.youtube.com/t/fact_sheet. Accessed November 2010

  23. Zink, M., Suh, K., Gu, Y., Kurose, J.: Watch global, cache local: YouTube network traffic at a campus network—Measurements and implications. In: MMCN ’08: Proceedings of SPIE/ACM Conference on Multimedia Computing and Networking (2008)

    Google Scholar 

Download references

Acknowledgements

We thank for the support provided by the Swiss National Science Foundation (SNSF) through the Swiss National Center of Competence in Research (NCCR) on Interactive Multimodal Information Management (IM)2.

Author information

Authors and Affiliations

  1. Idiap Research Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Joan-Isaac Biel & Daniel Gatica-Perez

Authors
  1. Joan-Isaac Biel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Gatica-Perez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joan-Isaac Biel .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Steven C.H. Hoi

  2. Kodak Research Laboratories, Lake Avenue 1999, Rochester, 14650, New York, USA

    Jiebo Luo

  3. Media Informatics and Multimedia Systems, University of Oldenburg, Escherweg 2, Oldenburg, 26121, Germany

    Susanne Boll

  4. School of Computer Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Dong Xu

  5. Dept. Computer Science and Engineering, Michigan State University, Engineering Building 3115, East Lansing, 48824, Michigan, USA

    Rong Jin

  6. Dept. Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong/PR China

    Irwin King

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this chapter

Cite this chapter

Biel, JI., Gatica-Perez, D. (2011). Call Me Guru: User Categories and Large-Scale Behavior in YouTube. In: Hoi, S., Luo, J., Boll, S., Xu, D., Jin, R., King, I. (eds) Social Media Modeling and Computing. Springer, London. https://doi.org/10.1007/978-0-85729-436-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-436-4_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-435-7

  • Online ISBN: 978-0-85729-436-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation