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Exploring Video Sharing Websites Content with Machine Learning

Exploring Video Sharing Websites Content with Machine Learning

Nan Zhao, Löic Baud, Patrick Bellot
Copyright: © 2014 |Volume: 5 |Issue: 4 |Pages: 20
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781466653702|DOI: 10.4018/ijdst.2014100103
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MLA

Zhao, Nan, et al. "Exploring Video Sharing Websites Content with Machine Learning." IJDST vol.5, no.4 2014: pp.31-50. http://doi.org/10.4018/ijdst.2014100103

APA

Zhao, N., Baud, L., & Bellot, P. (2014). Exploring Video Sharing Websites Content with Machine Learning. International Journal of Distributed Systems and Technologies (IJDST), 5(4), 31-50. http://doi.org/10.4018/ijdst.2014100103

Chicago

Zhao, Nan, Löic Baud, and Patrick Bellot. "Exploring Video Sharing Websites Content with Machine Learning," International Journal of Distributed Systems and Technologies (IJDST) 5, no.4: 31-50. http://doi.org/10.4018/ijdst.2014100103

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Abstract

This article studies the characteristics of content on video sharing websites. A better understanding on online video content can help to analyse Internet users' behaviour and improve the video-sharing service. We improved an existing graph-sampling algorithm so that it could be more adapted to sample over the video sharing websites. A newly category system is defined in this paper, which can be applied on many different video sharing websites for content analysis. We also implement machine learning to realize the content re-classification with the newly defined category system. The efficiency reaches at 90%. From the classified content analysis, we find the content category distribution is not constant, and nowadays, cultural goods content take about 70% over all the sampled videos.

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