Youtube Revisited: On the Importance of Correct Measurement Methodology

  • Ossi KarkulahtiEmail author
  • Jussi Kangasharju
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9053)


Measurements of large systems typically rely on sampling to keep the measurement effort practical. For example, Youtube’s video popularity has been measured by crawling either related videos or videos belonging to certain categories or by using a list of, e.g., the most recent videos as the data-source. In this paper we demonstrate that all these methods lead to a biased sample of data when compared to a random sample. We demonstrate the bias by comparing the differently sampled data sets in terms of different commonly used metrics, such as video popularity, age, length, or category. The results show that different sampling methods lead to significantly different values in the metrics, thus potentially leading to very different conclusions about the system under study. The goal of the paper is not to provide yet-another-set-of-numbers for YouTube; instead we seek to emphasize the importance of using correct measurement methodologies and understanding the inherent weaknesses of different methodologies.


Random String Music Video Video Popularity Popularity Distribution Video Length 
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.


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Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland

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