Understanding the External Links of Video Sharing Sites: Measurement and Analysis

Lai, Kee‐hung; Wang, Dan

doi:10.1109/tmm.2012.2225030

Cited by 8 publications

(13 citation statements)

References 31 publications

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“…For example, Cha et al [3] studied the popularity distribution and evolution for UGC content by collecting data traces from two large UGC video systems. Li et al [4] [14] showed that external links of video sharing sites can significantly affect video popularity and its dynamics. Some works also leverage the measurement of popularity dynamics to study more realistic schemes.…”

Section: Related Workmentioning

confidence: 99%

Modeling Dynamics of Online Video Popularity

Zhou

Chiu

et al. 2016

IEEE Trans. Multimedia

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Video popularity (measured by view count) over time is an essential reference for both online video providers and users. According to state-of-the-art works, video popularity is useful for system optimization, load generation, video caching and video recommendation. Thus, deeper understanding of video popularity evolution is very helpful for improving video service quality and providers' operating efficiency. The core question to be explored in this paper is what key factors govern online video popularity evolution? Through collaboration with our industry partner, Tencent Video, we obtain historical data of video view counts over a period of time, and observe their patterns. We then propose a stochastic fluid model, named as EvoModel, which captures two processes giving rise to different evolution patterns of a given video: (a) the information spreading process; and (b) the user reaction process. The driving forces for process (a) can be either via recommendation from the system directly, or word-of-mouth; the extent of the spread is governed by the intrinsic popularity of the video. The factor affecting the second process can be the historical data obtained from a real-world system. Furthermore, we discuss the feasibility of estimating model parameters and predicting popularity.

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Section: Related Workmentioning

confidence: 99%

Modeling Dynamics of Online Video Popularity

Zhou

Chiu

et al. 2016

IEEE Trans. Multimedia

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“…Then, researches on YouTube datasets have been a hot topic, which includes mining latent information of users [7][8][9] [10]. Most of previous work only uses video metadata, including links [10], geographic location [9], view count [7] and so on. However, in this work, besides video-accompanied metadata, visual content is also used for constructing social networks.…”

Section: Topic Participants In Internet Videosmentioning

confidence: 99%

Constructing Social Networks Based on Near-Duplicate Detection in YouTube Videos

Bai

Guo

et al. 2015

2015 IEEE International Conference on Multimedia Big Data

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With the video-sharing websites springing up, more and more people would like to upload and share either their own videos or remix others'. Meanwhile, they could view and comment the videos that they are interested in. Therefore, social networks among videos and users exist implicitly. In this work, we construct two types of social networks, video networks (VN) and topic participant networks (TPN), by utilizing videos, related metadata and near-duplicate detection. In the networks, the nodes denote the videos or users while the weights of the directed edges represent the correlation between the nodes. Then, several indices are defined to quantitatively evaluate the importance of the nodes in the networks. Experiments are conducted by using YouTube videos and corresponding metadata related with a specific event. Experimental results show that the analysis of social networks and indices fits the evolution of the event and the roll topic participants plays in spreading Internet videos very well. Finally, we extensionally investigate to utilize the network for recognizing important videos and participants, summarizing video datasets, and tracking an event with few videos.

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“…In a microblogging system, users can create and maintain social connections among each other, as well as subscribe to contents shared by others from external content sharing system, as followers [1]. Among the variety of contents to exchange, links to videos on video sharing sites are a popular type -users from microblogging exchanges are constituting a large portion of viewers in YouTube-like video sharing sites [2]. Popularity patterns of such socialized videos have greatly changed as follows:…”

Section: Introductionmentioning

confidence: 99%

“…The potential benefits are two-fold: (1) a video sharing site typically has no information about how video views propagate among its users, while a view propagation model could enable more effective view prediction; (2) the exchanges of video links in a microblogging system typically happen earlier than the actually video views on a video sharing site, and the time lag between both events can allow more timely and proactive deployment of videos. Based on our preliminary findings of the connection between the popularity of a video and how the video is shared in a microblogging system [8], in this paper, we focus on employing video microblog propagation patterns to improve the deployment of video services.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Internet-Scale Video Service Deployment Using Microblog-Based Prediction

Wang

Sun

2015

IEEE Trans. Parallel Distrib. Syst.

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Online microblogging has been very popular in today's Internet, where users follow other people they are interested in and exchange information between themselves. Among these exchanges, video links are a representative type on a microblogging site. The impact is fundamental -not only are viewers in a video service directly coming from the microblog sharing and recommendation, but also are the users in the microblogging site representing a promising sample to all the viewers. It is intriguing to study a proactive service deployment for such videos, using the propagation patterns of microblogs. Based on extensive traces from Youku and Tencent Weibo, a popular video sharing site and a favored microblogging system, we explore how video propagation patterns in the microblogging system are correlated with video popularity on the video sharing site. Using influential factors summarized from the measurement studies, we further design a neural network-based learning framework to predict the number of potential viewers and their geographic distribution. We then design proactive video deployment algorithms based on the prediction framework, which not only determines the upload capacities of servers in different regions, but also strategically replicates videos to these regions to serve users. Our PlanetLab-based experiments verify the effectiveness of our design.

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Understanding the External Links of Video Sharing Sites: Measurement and Analysis

Cited by 8 publications

References 31 publications

Modeling Dynamics of Online Video Popularity

Modeling Dynamics of Online Video Popularity

Constructing Social Networks Based on Near-Duplicate Detection in YouTube Videos

Enhancing Internet-Scale Video Service Deployment Using Microblog-Based Prediction

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