Probability, statistics, and queueing theory with computer science applications
Probability, statistics, and queueing theory with computer science applications
Youtube traffic characterization: a view from the edge
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Analyzing the video popularity characteristics of large-scale user generated content systems
IEEE/ACM Transactions on Networking (TON)
Predicting the popularity of online content
Communications of the ACM
The impact of YouTube recommendation system on video views
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
The tube over time: characterizing popularity growth of youtube videos
Proceedings of the fourth ACM international conference on Web search and data mining
Characterizing Web-Based Video Sharing Workloads
ACM Transactions on the Web (TWEB)
Characterizing and modelling popularity of user-generated videos
Performance Evaluation
On the prediction of popularity of trends and hits for user generated videos
Proceedings of the sixth ACM international conference on Web search and data mining
Demystifying porn 2.0: a look into a major adult video streaming website
Proceedings of the 2013 conference on Internet measurement conference
Towards modeling popularity of microblogs
Frontiers of Computer Science: Selected Publications from Chinese Universities
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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.