Abstract
Web Video continues to gain importance not only in many areas of computer science but in society in general. With the growth in numbers, both of videos, viewers, and views, there arise several technical challenges. In order to address them effectively, the properties of Web Video in general need to be known. There is however comparatively little analysis of these properties. In this paper, we present insights gained from the analysis of a data set containing the meta data of over 100 million videos from YouTube. We were able to confirm common wisdom about the relationship between video duration and user engagement and show the extreme long tail of the distribution of video views overall. Such data can be beneficial in making informed decisions regarding strategies for large scale video storage, delivery, processing and retrieval.
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Notes
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- 5.
With ‘age’, we denote the difference in days between the date the video was uploaded and the date the metadata of the video was harvested.
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Average(views/likes): 332.9592, median(views/likes): 331.0722.
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Average(likes/dislikes): 17.2896, median(likes/dislikes): 16.8257.
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Average(views/likes): 201.6157, median(views/likes): 195.7886.
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Average(likes/dislikes): 15.4702, median(likes/dislikes): 14.6817.
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Acknowledgements
This work was partly supported by the Chist-Era project IMOTION with contributions from the Swiss National Science Foundation (SNSF, contract no. 20CH21_151571).
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Rossetto, L., Schuldt, H. (2018). The Long Tail of Web Video. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10705. Springer, Cham. https://doi.org/10.1007/978-3-319-73600-6_26
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DOI: https://doi.org/10.1007/978-3-319-73600-6_26
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