Understanding the Characteristics of Internet Short Video Sharing: A YouTube-Based Measurement Study

Cheng, Xu; Liu, Jiangchuan; Dale, C.

doi:10.1109/tmm.2013.2265531

Cited by 280 publications

(253 citation statements)

References 18 publications

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“…Figure 7 shows the Rank-frequency distribution for the 6 categories that showed the most interesting patterns. Previous studies (Abhari and Soraya, 2010;Cheng et al, 2007) showed that although requests for popular YouTube videos follow a Zipf-like distribution, a Weibull distribution fits better because of the heavy tail section, which indicates a large number of very unpopular videos in YouTube. After considering video categories, only News videos follow a Weibull distribution for the first 80% of the videos, because of the comparatively flatter head section of News access pattern.…”

Section: Current Uploading Ratementioning

confidence: 97%

“…2.5 million YouTube videos were obtained using related video links (Cheng et al, 2007). Access patterns of the popular videos did follow a Zipf-like distribution, in spite of having a heavy-tailed section in the distribution curve.…”

Section: Related Workmentioning

confidence: 99%

“…In 2007, Music was in the top position in number of uploaded videos followed by Entertainment, Comedy, Sports and Film (Cheng et al, 2007). Manual sampling revealed that these categories are dominated by UCC rather than UGC, so most of the videos in YouTube were actually UCC.…”

Section: Current Uploading Ratementioning

confidence: 99%

“…YouTube was the 4 th most accessed Internet site in 2007 (Cheng et al, 2007), and its use was increasing over time in a power-law manner. Recent studies continue to support two central observations: 1) increasing number of videos and users (Ding et al, 2011;Siersdorfer et al, 2010) and 2) dissatisfying experiences of users in watching YouTube videos (Khemmarat et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Popularity Growth Patterns of YouTube Videos - A Category-based Study

Chowdhury

Makaroff

2013

Proceedings of the 9th International Conference on Web Information Systems and Technologies

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Abstract:Understanding the growth pattern of content popularity has become a subject of immense interest to Internet service providers, content makers and on-line advertisers. This understanding is important for the sustainable deployment of content distribution systems. A significant amount of research has been done in analyzing the popularity growth patterns of YouTube videos. Unfortunately, little work has been done that investigates the popularity patterns of YouTube videos based on video object category. In this paper, we perform an in-depth analysis of the popularity pattern of YouTube videos, considering video categories. We find that the time varying popularity of different YouTube categories are different from each other. For some categories, views at early ages can be used to predict future popularity, whereas for some other categories, predicting future popularity is a challenging task and requires more sophisticated techniques (e.g. time-series clustering). The outcomes of these analyses can be instrumental towards designing a reliable workload generator, which can be further used to evaluate different caching policies and distribution mechanism for YouTube and similar sites.

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Section: Current Uploading Ratementioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Current Uploading Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Popularity Growth Patterns of YouTube Videos - A Category-based Study

Chowdhury

Makaroff

2013

Proceedings of the 9th International Conference on Web Information Systems and Technologies

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“…It can be used to derive the probability that the content item with a specific popularity index (e.g., the X th most popular item) will be requested. Many models have been proposed for modelling the popularity distribution of a multimedia service, including Zipf [17], Zipf-Mandelbrot [18], stretched exponential [19], Zipf with exponential cut-off tail [20], power-law with exponential cut-off tail [21], log-logistic [22] and Weibull [22].…”

Section: Related Workmentioning

confidence: 99%

Towards a predictive cache replacement strategy for multimedia content

Famaey

Iterbeke

Wauters

et al. 2013

Journal of Network and Computer Applications

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In recent years, telecom operators have been moving away from traditional broadcast-driven television, towards IP-based interactive and on-demand multimedia services. Consequently, multicast is no longer sufficient to limit the amount of generated traffic in the network. In order to prevent an explosive growth in traffic, caches can be strategically placed throughout the content delivery infrastructure. As the size of caches is usually limited to only a small fraction of the total size of all content items, it is important to accurately predict future content popularity. Traditional caching strategies only take into account the past when deciding what content to cache. Recently, a trend towards novel strategies that actually try to predict future content popularity has arisen. In this article, we ascertain the viability of using popularity prediction in realistic multimedia content caching scenarios. The proposed generic popularity prediction algorithm is capable of predicting future content popularity, independent of specific content and service characteristics. Additionally, a novel cache replacement strategy, which employs the popularity prediction algorithm when making its decisions, is introduced. A detailed evaluation, based on simulation results using trace files from an actual deployed Video on Demand service, was performed. The evaluation results are used to determine the merits of popularity-based caching compared to traditional strategies. Additionally, the synergy between several parameters, such as cache size and prediction window, is investigated. Results show that the proposed prediction-based caching strategy has the potential to significantly outperform state-of-the-art traditional strategies. Specifically, the evaluated Video on Demand scenario showed a performance increase of up to 20% in terms of cache hit rate.

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The role of online videos in research communication: A content analysis of YouTube videos cited in academic publications

Kousha

Thelwall

Abdoli³

2012

J Am Soc Inf Sci Tec

117

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Although there is some evidence that online videos are increasingly used by academics for informal scholarly communication and teaching, the extent to which they are used in published academic research is unknown. This article explores the extent to which YouTube videos are cited in academic publications and whether there are significant broad disciplinary differences in this practice. To investigate, we extracted the URL citations to YouTube videos from academic publications indexed by Scopus. A total of 1,808 Scopus publications cited at least one YouTube video and there was a steady upward growth in citing online videos within scholarly publications from 2006 to 2011, with YouTube citations being most common within arts and humanities (0.3%) and the social sciences (0.2%). A content analysis of 551 YouTube videos cited by research articles indicated that in science (78%) and in medicine and health sciences (77%) over three quarters of the cited videos had either direct scientific (e.g., laboratory experiments) or scientific-related contents (e.g., academic lectures or education), whereas in the arts and humanities about 80% of the YouTube videos had art, culture or history themes and in the social sciences about 63% of the videos were related to news, politics, advertisements and documentaries. This shows both disciplinary differences and the wide variety of innovative research communication uses found for videos within the different subject areas.

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Understanding the Characteristics of Internet Short Video Sharing: A YouTube-Based Measurement Study

Cited by 280 publications

References 18 publications

Popularity Growth Patterns of YouTube Videos - A Category-based Study

Popularity Growth Patterns of YouTube Videos - A Category-based Study

Towards a predictive cache replacement strategy for multimedia content

The role of online videos in research communication: A content analysis of YouTube videos cited in academic publications

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