Youtube Revisited: On the Importance of Correct Measurement Methodology
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.
KeywordsRandom String Music Video Video Popularity Popularity Distribution Video Length
- 1.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 the 7th ACM SIGCOMM Conference on Internet measurement, pp. 1–14. ACM (2007)Google Scholar
- 3.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. ACM (2007)Google Scholar
- 4.Krishnamurthy, B., Gill, P., Arlitt, M.: A few chirps about twitter. In: Proceedings of the Tworkshop on Online Social Networks, pp. 19–24. ACM (2008)Google Scholar
- 7.Valancius, V., Laoutaris, N., Massoulié, L., Diot, C., Rodriguez, P.: Greening the internet with nano data centers. In: Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, pp. 37–48. ACM (2009)Google Scholar
- 8.Zhou, J., Li, Y., Adhikari, V.K., Zhang, Z.-L.: Counting youtube videos via random prefix sampling. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 371–380. ACM (2011)Google Scholar