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- Borghol, Youmna, et al.
(författare)
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Characterizing and modelling popularity of user-generated videos
- 2011
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Ingår i: Performance evaluation (Print). - : Elsevier. - 0166-5316 .- 1872-745X. ; 68:11, s. 1037-1055
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Tidskriftsartikel (refereegranskat)abstract
- This paper develops a framework for studying the popularity dynamics of user-generated videos, presents a characterization of the popularity dynamics, and proposes a model that captures the key properties of these dynamics. We illustrate the biases that may be introduced in the analysis for some choices of the sampling technique used for collecting data; however, sampling from recently-uploaded videos provides a dataset that is seemingly unbiased. Using a dataset that tracks the views to a sample of recently-uploaded YouTube videos over the first eight months of their lifetime, we study the popularity dynamics. We find that the relative popularities of the videos within our dataset are highly non-stationary, owing primarily to large differences in the required time since upload until peak popularity is finally achieved, and secondly to popularity oscillation. We propose a model that can accurately capture the popularity dynamics of collections of recently-uploaded videos as they age, including key measures such as hot set churn statistics, and the evolution of the viewing rate and total views distributions over time.
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- Borghol, Youmna, et al.
(författare)
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The Untold Story of the Clones: Content-agnostic Factors that Impact YouTube Video Popularity
- 2012
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Ingår i: Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2012. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450314626 ; , s. 1186-1194
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Konferensbidrag (refereegranskat)abstract
- Video dissemination through sites such as YouTube can have widespread impacts on opinions, thoughts, and cultures. Not all videos will reach the same popularity and have the same impact. Popularity differences arise not only because of differences in video content, but also because of other "content-agnostic" factors. The latter factors are of considerable interest but it has been difficult to accurately study them. For example, videos uploaded by users with large social networks may tend to be more popular because they tend to have more interesting content, not because social network size has a substantial direct impact on popularity.In this paper, we develop and apply a methodology that is able to accurately assess, both qualitatively and quantitatively, the impacts of various content-agnostic factors on video popularity. When controlling for video content, we observe a strong linear "rich-get-richer" behavior, with the total number of previous views as the most important factor except for very young videos. The second most important factor is found to be video age. We analyze a number of phenomena that may contribute to rich-get-richer, including the first-mover advantage, and search bias towards popular videos. For young videos we find that factors other than the total number of previous views, such as uploader characteristics and number of keywords, become relatively more important. Our findings also confirm that inaccurate conclusions can be reached when not controlling for content.
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- Borghol, Youmna, et al.
(författare)
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Toward Efficient On-demand Streaming with BitTorrent
- 2010
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Ingår i: NETWORKING 2010. - Berlin, Heidelberg : Springer. - 9783642129629 - 3642129625 ; , s. 53-66
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Bokkapitel (refereegranskat)abstract
- This paper considers the problem of adapting the BitTorrent protocol for on-demand streaming. BitTorrent is a popular peer-to-peer file sharing protocol that efficiently accommodates a large number of requests for file downloads. Two components of the protocol, namely the Rarest-First piece selection policy and the Tit-for-Tat algorithm for peer selection, are acknowledged to contribute toward the protocol's efficiency with respect to time to download files and its resilience to freeriders. Rarest-First piece selection, however, does not augur well for on-demand streaming. In this paper, we present a new adaptive Window-based piece selection policy that achieves a balance between the system scalability provided by the Rarest-First algorithm and the necessity of In-Order pieces for seamless media playback. We also show that this simple modification to the piece selection policy allows the system to be efficient with respect to utilization of available upload capacity of participating peers, and does not break the Tit-for-Tat incentive scheme which provides resilience to freeriders.
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- Sebastien, Ardon, et al.
(författare)
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MARCH: a distributed content adaptation architecture
- 2003
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Ingår i: International Journal of Communication Systems: Special Issue: Wireless Access to the Global Internet: Mobile Radio Networks and Satellite Systems. ; March:1
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Tidskriftsartikel (refereegranskat)
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