Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)
Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)
Predicting the popularity of online content
Communications of the ACM
Who uses web search for what: and how
Proceedings of the fourth ACM international conference on Web search and data mining
#TwitterSearch: a comparison of microblog search and web search
Proceedings of the fourth ACM international conference on Web search and data mining
The tube over time: characterizing popularity growth of youtube videos
Proceedings of the fourth ACM international conference on Web search and data mining
User Groups in Social Networks: An Experimental Study on YouTube
HICSS '11 Proceedings of the 2011 44th Hawaii International Conference on System Sciences
Democrats, republicans and starbucks afficionados: user classification in twitter
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamical classes of collective attention in twitter
Proceedings of the 21st international conference on World Wide Web
Understanding experts' and novices' expertise judgment of twitter users
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
TasteWeights: a visual interactive hybrid recommender system
Proceedings of the sixth ACM conference on Recommender systems
Political polarization and popularity in online participatory media: an integrated approach
Proceedings of the first edition workshop on Politics, elections and data
Using early view patterns to predict the popularity of youtube videos
Proceedings of the sixth ACM international conference on Web search and data mining
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Inferring audience partisanship for YouTube videos
Proceedings of the 22nd international conference on World Wide Web companion
Social resilience in online communities: the autopsy of friendster
Proceedings of the first ACM conference on Online social networks
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By combining multiple social media datasets, it is possible to gain insight into each dataset that goes beyond what could be obtained with either individually. In this paper we combine user-centric data from Twitter with video-centric data from YouTube to build a rich picture of who watches and shares what on YouTube. We study 87K Twitter users, 5.6 million YouTube videos and 15 million video sharing events from user-, video- and sharing-event-centric perspectives. We show that features of Twitter users correlate with YouTube features and sharing-related features. For example, urban users are quicker to share than rural users. We find a superlinear relationship between initial Twitter shares and the final amounts of views. We discover that Twitter activity metrics play more role in video popularity than mere amount of followers. We also reveal the existence of correlated behavior concerning the time between video creation and sharing within certain timescales, showing the time onset for a coherent response, and the time limit after which collective responses are extremely unlikely. Response times depend on the category of the video, suggesting Twitter video sharing is highly dependent on the video content. To the best of our knowledge, this is the first large-scale study combining YouTube and Twitter data, and it reveals novel, detailed insights into who watches (and shares) what on YouTube, and when.