Abstract
Our work merges user-centric data from Twitter with video-centric data from YouTube to investigate the ties between predictability of video sharing and the social context of video uploaders. It provides a combination of social media datasets, giving insights than neither dataset (social network and media service) individually gives. We develop an accurate model to predict future popularity of a video resource given features of the underlying network of its initial sharer. The set of features we propose and analyze are based on the notion of influence score of a user and its fluctuation through time, as well as the distance of content interests among users for both datasets. We discover that the latter feature plays an important role in video popularity prediction, suggesting high dependence of video sharing via Twitter on the video content itself. We proceed to incorporate our prediction model into a mechanism for content delivery, achieving substantial improvement of the user experience.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abisheva, A., Garimella, V.R.K., Garcia, D., Weber, I.: Who watches (and shares) what on YouTube? And when?: Using Twitter to understand Youtube viewership. IN: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM 2014, New York, NY, USA, 24–28 Feb 2014, pp. 593–602 (2014). doi:10.1145/2556195.2566588
Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings of the 4th International Conference on Web Search and Web Data Mining, WSDM 2011, Hong Kong, China, 9–12 Feb 2011, pp. 65–74 (2011). doi:10.1145/1935826.1935845
Brodersen, A., Scellato, S., Wattenhofer, M.: YouTube around the world: geographic popularity of videos. In: Proceedings of the 21st World Wide Web Conference, WWW 2012, Lyon, France, 16–20 April 2012, pp. 241–250 (2012). doi:10.1145/2187836.2187870
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y., Moon, S.B.: I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, IMC 2007, San Diego, California, USA, 24–26 Oct 2007, pp. 1–14 (2007). doi:10.1145/1298306.1298309
Cheng, J., Adamic, L.A., Dow, P.A., Kleinberg, J.M., Leskovec, J.: Can cascades be predicted? In: Proceedings of the 23rd International World Wide Web Conference, WWW 2014, Seoul, Republic of Korea, 7–11 April 2014, pp. 925–936 (2014). doi:10.1145/2566486.2567997
Dow, P.A., Adamic, L.A., Friggeri, A.: The anatomy of large Facebook cascades. In: Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013, Cambridge, Massachusetts, USA, 8–11 July 2013
Galuba, W., Aberer, K., Chakraborty, D., Despotovic, Z., Kellerer, W.: Outtweeting the Twitterers—Predicting Information Cascades in Microblogs. In: Proceedings of the 3rd Workshop on Online Social Networks, WOSN 2010, Boston, MA, USA, 22 June 2010
Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in Twitter. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, March 28–April 1, 2011 (Companion Volume), pp. 57–58 (2011). doi:10.1145/1963192.1963222
Jenders, M., Kasneci, G., Naumann, F.: Analyzing and predicting viral tweets. In: Proceedings of the 22nd International World Wide Web Conference, WWW 2013, Rio de Janeiro, Brazil, 13–17 May 2013, Companion Volume, pp. 657–664 (2013)
Kilanioti, I.: Improving multimedia content delivery via augmentation with social information. The Social Prefetcher approach. IEEE Trans. Multimedia 17(9), 1460–1470 (2015). doi:10.1109/TMM.2015.2459658
Kilanioti, I., Papadopoulos, G.A.: Socially-aware multimedia content delivery for the cloud. In: Proceedings of the 8th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2015, Limassol, Cyprus, 7–10 Dec 2015, pp. 300–309 (2015). doi:10.1109/UCC.2015.48
Kupavskii, A., Ostroumova, L., Umnov, A., Usachev, S., Serdyukov, P., Gusev, G., Kustarev, A.: Prediction of retweet cascade size over time. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, Maui, HI, USA, October 29–November 02, 2012, pp. 2335–2338 (2012). doi:10.1145/2396761.2398634
Kwak, H., Lee, C., Park, H., Moon, S.B.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, 26–30 April 2010, pp. 591–600 (2010). doi:10.1145/1772690.1772751
Ma, Z., Sun, A., Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter. JASIST 64(7), 1399–1410 (2013). doi:10.1002/asi.22844
Petrovic, S., Osborne, M., Lavrenko, V.: RT to win! Predicting message propagation in Twitter. In: Proceedings of the 5th International Conference on Weblogs and Social Media, ICWSM 2011, Barcelona, Catalonia, Spain, 17–21 July 2011
Rodrigues, T., Benevenuto, F., Cha, M., Gummadi, P.K., Almeida, V.A.F.: On word-of-mouth based discovery of the web. In: Proceedings of the 11th ACM SIGCOMM Conference on Internet Measurement, IMC 2011, Berlin, Germany, 2–4 Nov 2011, pp. 381–396 (2011). doi:10.1145/2068816.2068852
Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In: Proceedings of the 2nd IEEE International Conference on Social Computing, SocialCom / IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2010, Minneapolis, Minnesota, USA, 20–22 Aug 2010, pp. 177–184 (2010). doi:10.1109/SocialCom.2010.33
Szabó, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53(8), 80–88 (2010). doi:10.1145/1787234.1787254
Tsur, O., Rappoport, A.: What’s in a hashtag? Content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the 5th International Conference on Web Search and Web Data Mining, WSDM 2012, Seattle, WA, USA, 8–12 Feb 2012, pp. 643–652 (2012). doi:10.1145/2124295.2124320
Yang, J., Leskovec, J.: Patterns of temporal variation in online media. In: Proceedings of the 4th International Conference on Web Search and Web Data Mining, WSDM 2011, Hong Kong, China, 9–12 Feb 2011, pp. 177–186 (2011). doi:10.1145/1935826.1935863
Yang, S., Zha, H.: Mixture of mutually exciting processes for viral diffusion. In: Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16–21 June 2013, pp. 1–9 (2013)
Zaman, T.R., Herbrich, R., Van Gael, J., Stern, D.: Predicting information spreading in Twitter. In: Proceedings of the Workshop on Computational Social Science and the Wisdom of Crowds, Nips, vol. 104, pp. 17, 599–601 (2010)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this chapter
Cite this chapter
Kilanioti, I., Papadopoulos, G.A. (2016). Predicting Video Virality on Twitter. In: Pop, F., Kołodziej, J., Di Martino, B. (eds) Resource Management for Big Data Platforms. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-44881-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-44881-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44880-0
Online ISBN: 978-3-319-44881-7
eBook Packages: Computer ScienceComputer Science (R0)