Adiya Abisheva
Ingmar Weber
ABSTRACT
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.
- F. Al Zamal, W. Liu, and D. Ruths. Homophily and latent attribute inference: Inferring latent attributes of twitter users from neighbors. In ICWSM, 2012.Google Scholar
- S. Asur, B. A. Huberman, G. Szabo, and C. Wang. Trends in Social Media: Persistence and Decay. In ICWSM, pages 434--437, 2011.Google ScholarCross Ref
- A.-L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435:207, 2005.Google ScholarCross Ref
- P. Barbera. Birds of the Same Feather Tweet Together. Bayesian Ideal Point Estimation Using Twitter Data, 2012. Forthcoming.Google Scholar
- S. Bostandjiev, J. O'Donovan, and T. Höllerer. Tasteweights: A visual interactive hybrid recommender system. In RecSys, pages 35--42, 2012. Google ScholarDigital Library
- M. Cha, H. Haddadi, F. Benevenuto, and P. K. Gummadi. Measuring user influence in twitter: The million follower fallacy. In ICWSM, 2010.Google ScholarCross Ref
- M. Conover, J. Ratkiewicz, M. Francisco, B. Gonçalves, F. Menczer, and A. Flammini. Political polarization on twitter. In ICWSM, 2011.Google Scholar
- M. D. Conover, B. Gonc, A. Flammini, and F. Menczer. Partisan Asymmetries in Online Political Activity. EPJ DataScience, 1(6):1--17, 2012.Google Scholar
- M. D. Conover, B. Goncalves, J. Ratkiewicz, A. Flammini, and F. Menczer. Predicting the Political Alignment of Twitter Users. In PASSAT-SocialCom, pages 192--199, 2011.Google ScholarCross Ref
- R. Crane, F. Schweitzer, and D. Sornette. Power law signature of media exposure in human response waiting time distributions. Physical Review E, 81(5):056101, 2010.Google ScholarCross Ref
- R. Crane and D. Sornette. Robust dynamic classes revealed by measuring the response function of a social system. PNAS, 105(41):15649--15653, 2008.Google ScholarCross Ref
- F. Figueiredo, F. Benevenuto, and J. M. Almeida. The tube over time: Characterizing popularity growth of youtube videos. In WSDM, pages 745--754, 2011. Google ScholarDigital Library
- D. Garcia, P. Mavrodiev, and F. Schweitzer. Social resilience in online communities: The autopsy of friendster. 1st ACM Conference on Online Social Networks (Forthcoming), 2013. Google ScholarDigital Library
- D. Garcia, F. Mendez, U. Serdült, and F. Schweitzer. Political polarization and popularity in online participatory media. In PLEAD, pages 3--10, Nov. 2012. Google ScholarDigital Library
- D. Gayo-Avello. Nepotistic relationships in twitter and their impact on rank prestige algorithms. Inf. Process. Manage., 49(6):1250--1280, 2013.Google ScholarCross Ref
- S. Goel, J. M. Hofman, and M. I. Sirer. Who does what on the web: A large-scale study of browsing behavior. In ICWSM, 2012.Google Scholar
- P. I. Good. Permutation, Parametric, and Bootstrap Tests of Hypotheses. Springer, 3 edition, 2005. Google ScholarDigital Library
- S. Havlin. The distance between zipf plots. Physica A Statistical and Theoretical Physics, 216(1-2):148--150, 1995.Google ScholarCross Ref
- S. R. Kairam, M. R. Morris, J. Teevan, D. J. Liebling, and S. T. Dumais. Towards supporting search over trending events with social media. In ICWSM, 2013.Google Scholar
- M. Karsai, K. Kaski, A.-L. Barabási, and J. Kertész. Universal features of correlated bursty behaviour, May 2012.Google Scholar
- G. Karypis. Cluto: A clustering toolkit, 2002. http://www.cs.umn.edu/~cluto.Google Scholar
- J. Kulshrestha, F. Kooti, A. Nikravesh, and P. K. Gummadi.Geographic dissection of the twitter network. In ICWSM, 2012.Google Scholar
- M. S. S. Laine and G. Ercal. User Groups in Social Networks: An Experimental Study on YouTube. In 2011 44th Hawaii International Conference on System Sciences, pages 1--10, Jan. 2011. Google ScholarDigital Library
- J. Lehmann, B. Gonçalves, J. J. Ramasco, and C. Cattuto. Dynamical classes of collective attention in twitter. In WWW, page 251, New York, New York, USA, 2012. Google ScholarDigital Library
- Q. V. Liao, C. Wagner, P. Pirolli, and W.-T. Fu. Understanding experts' and novices' expertise judgment of twitter users. In CHI, pages 2461--2464, 2012. Google ScholarDigital Library
- A. Mislove, S. Lehmann, Y.-y. Ahn, J.-p. Onnela, and J. N. Rosenquist. Understanding the demographics of twitter users. In ICWSM, pages 554--557, 2011.Google Scholar
- M. Pennacchiotti and A.-M. Popescu. Democrats, republicans and starbucks afficionados: User classification in twitter. In KDD, pages 430--438, 2011. Google ScholarDigital Library
- H. Pinto, J. M. Almeida, and M. A. Gonçalves. Using early view patterns to predict the popularity of youtube videos. In WSDM, pages 365--374, 2013. Google ScholarDigital Library
- L. Qiu, Q. Tang, and A. B. Whinston. Two formulas for success in social media: Social learning and network effects, 2013. Forthcoming.Google Scholar
- D. Soysa, D. Chen, O. Au, and A. Bermak. Predicting youtube content popularity via facebook data: A network spread model for optimizing multimedia delivery. In CIDM, pages 214--221, 2013.Google ScholarCross Ref
- G. Szabo and B. A. Huberman. Predicting the popularity of online content. Communications of the ACM, 53(8):80, 2010. Google ScholarDigital Library
- J. Teevan, D. Ramage, and M. R. Morris. #twittersearch: A comparison of microblog search and web search. In WSDM, pages 35--44, 2011. Google ScholarDigital Library
- A. Ulges, D. Borth, and M. Koch. Content analysis meets viewers: linking concept detection with demographics on youtube. IJMIR, 2(2):145--157, 2013.Google Scholar
- C. Wang and B. A. Huberman. Long trend dynamics in social media. EPJ Data Science, page 8, May 2012.Google Scholar
- I. Weber, V. R. K. Garimella, and E. Borra. Political search trends. In SIGIR, pages 1012--1012. ACM, 2012. Google ScholarDigital Library
- I. Weber, V. R. K. Garimella, and E. Borra. Inferring audience partisanship for youtube videos. In WWW, pages 43--44, 2013. Google ScholarDigital Library
- I. Weber, V. R. K. Garimella, and A. Teka. Political hashtag trends. In ECIR, pages 857--860, 2013. Google ScholarDigital Library
- I. Weber and A. Jaimes. Who uses web search for what: and how. In WSDM, pages 15--24, 2011. Google ScholarDigital Library
- Y. Wu, C. Zhou, J. Xiao, J. Kurths, and H. J. Schellnhuber. Evidence for a bimodal distribution in human communication. Proceedings of the National Academy of Sciences, 107(44):18803--18808, Oct. 2010.Google ScholarCross Ref
Index Terms
- Who watches (and shares) what on youtube? and when?: using twitter to understand youtube viewership
Recommendations
Twitter-driven YouTube Views: Beyond Individual Influencers
MM '14: Proceedings of the 22nd ACM international conference on MultimediaThis paper proposes a novel method to predict increases in YouTube viewcount driven from the Twitter social network. Specifically, we aim to predict two types of viewcount increases: a sudden increase in viewcount (named as Jump), and the viewcount ...
YouTube Recommendations and Effects on Sharing Across Online Social Platforms
CSCWIn January 2019, YouTube announced its platform would exclude potentially harmful content from video recommendations while allowing such videos to remain on the platform. While this action is intended to reduce YouTube's role in propagating such content, ...
The role of twitter in youtube videos diffusion
WISE'12: Proceedings of the 13th international conference on Web Information Systems EngineeringUnderstanding the effects of social cascading on streaming media is of great importance to Web information system engineering. Given the large amount of available videos, it is often difficult for users to discover interesting content. Relying on the ...
Comments