Video interactions in online video social networks

Benevenuto, Fabrí­cio; Rodrigues, Tiago; Almeida, Virgílio A. F.; Almeida, Jussara M.; Ross, Keith W.

doi:10.1145/1596990.1596994

Cited by 64 publications

(42 citation statements)

References 44 publications

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“…Gill et al [8] and Zink et al [14] studied the popularity of YouTube content within large edge networks by collecting network traces from the interconnection between their respective university campus access network and the Internet. Other studies have examined such issues as user interaction, and the impacts of search mechanisms and social networks on video views [1], [4], [7].…”

Section: Related Workmentioning

confidence: 99%

Revisiting Popularity Characterization and Modeling of User-Generated Videos

Islam

Eager

Carlsson

et al. 2013

2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems

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Abstract-This paper presents new results on characterization and modeling of user-generated video popularity evolution, based on a recent complementary data collection for videos that were previously the subject of an eight month data collection campaign during 2008/09. In particular, during 2011, we collected two contiguous months of weekly view counts for videos in two separate 2008/09 datasets, namely the "recently-uploaded" and the "keyword-search" datasets. These datasets contain statistics for videos that were uploaded within 7 days of the start of data collection in 2008 and videos that were discovered using a keyword search algorithm in 2008, respectively. Our analysis shows that the average weekly view count for the recentlyuploaded videos had not decreased by the time of the second measurement period, in comparison to the middle and later portions of the first measurement period. The new data is used to evaluate the accuracy of a previously proposed model for synthetic view count generation for time periods that are substantially longer than previously considered. We find that the model yielded distributions of total (lifetime) video view counts that match the empirical distributions; however, significant differences between the model and empirical data were observed with respect to other metrics. These differences appear to arise because of particular popularity characteristics that change over time rather than being week-invariant as assumed in the model.

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Section: Related Workmentioning

confidence: 99%

Revisiting Popularity Characterization and Modeling of User-Generated Videos

Islam

Eager

Carlsson

et al. 2013

2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems

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“…Studies have examined the characteristics of user-generated video files [6,7,11,16,21], use of social networking features in video-sharing services [13,16], the structure of YouTube's "friend" network [15], the use of the "video response" feature of YouTube [4], the popularity characteristics of user-generated videos [5,6,10,11,16,21], and also models for user-generated video popularity prediction [14,19]. Here, we restrict attention mostly to related work on popularity characterization and modelling for user-generated videos.…”

Section: Related Workmentioning

confidence: 99%

Characterizing and modelling popularity of user-generated videos

Borghol

Mitra

Ardon

et al. 2011

Performance Evaluation

102

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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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“…In these sharing systems, video content serves both as a means of communicating a simple or complex story (using implicit or explicit cinematic rules) and as a catalyst for communication among third-party viewers of that content [4] [7].…”

Section: Introductionmentioning

confidence: 99%

Creating and sharing personalized time-based annotations of videos on the web

Guimarães

César

Bulterman

2010

Proceedings of the 10th ACM Symposium on Document Engineering

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This paper introduces a multimedia document model that can structure community comments about media. In particular, we describe a set of temporal transformations for multimedia documents that allow end-users to create and share personalized timed-text comments on third party videos. The benefit over current approaches lays in the usage of a rich captioning format that is not embedded into a specific video encoding format. Using as example a Web-based video annotation tool, this paper describes the possibility of merging video clips from different video providers into a logical unit to be captioned, and tailoring the annotations to specific friends or family members. In addition, the described transformations allow for selective viewing and navigation through temporal links, based on end-users' comments. We also report on a predictive timing model for synchronizing unstructured comments with specific events within a video(s). The contributions described in this paper bring significant implications to be considered in the analysis of rich media social networking sites and the design of next generation video annotation tools.

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Video interactions in online video social networks

Cited by 64 publications

References 44 publications

Revisiting Popularity Characterization and Modeling of User-Generated Videos

Revisiting Popularity Characterization and Modeling of User-Generated Videos

Characterizing and modelling popularity of user-generated videos

Creating and sharing personalized time-based annotations of videos on the web

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