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

  • Joan-Isaac Biel
  • Daniel Gatica-Perez


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%.


User Behavior Video Content Special User Online Video Social Media Site 
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.



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.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Idiap Research InstituteEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

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