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Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries

Published: 10 March 2022 Publication History

Abstract

How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the Phoenix-R model which captures the popularity dynamics of individual objects. Phoenix-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.

References

[1]
Anderson, A., Kumar, R., Tomkins, A., Vassilvitski, S.: Dynamics of Repeat Consumption. In: Proc. WWW (2014)
[2]
Bauckhage, C., Kersting, K., Hadiji, F.: Mathematical Models of Fads Explain the Temporal Dynamics of Internet Memes. In: Proc. ICWSM (2013)
[3]
Celma, O.: Music Recommendation and Discovery in the Long Tail, 1st edn. Springer (2010)
[4]
Cha, M., Mislove, A., Adams, B., Gummadi, K.P.: Characterizing social cascades in flickr. In: Proc. WOSN (2008)
[5]
Cha, M., Mislove, A., Gummadi, K.P.: A Measurement-Driven Analysis of Information Propagation in the Flickr Social Network. In: Proc. WWW (2009)
[6]
Crane R. and Sornette D. Robust Dynamic Classes Revealed by Measuring the Response Function of a Social System Proceedings of the National Academy of Sciences 2008 105 41 15649-15653
[7]
Du P., Kibbe W.A., and Lin S.M. Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching Bioinformatics 2006 22 17 2059-2065
[8]
Duong, Q., Goel, S., Hofman, J., Vassilvitskii, S.: Sharding social networks. In: Proc. WSDM (2013)
[9]
Figueiredo, F., Benevenuto, F., Almeida, J.: The Tube Over Time: Characterizing Popularity Growth of YouTube Videos. In: Proc. WSDM (2011)
[10]
Hansen M.H. and Yu B. Model Selection and the Principle of Minimum Description Length Journal of the American Statistical Association 2001 96 454 746-774
[11]
Hauger, D., Schedl, M., Kosir, A., Tkalci, M.: The Million Musical Tweets Dataset: What Can we Learn from Microblogs. In: Proc. ISMIR (2013)
[12]
Hethcote H.W. The Mathematics of Infectious Diseases SIAM Review 2000 42 4 599-653
[13]
Hu, Q., Wang, G., Yu, P.S.: Deriving Latent Social Impulses to Determine Longevous Videos. In: Proc. WWW (2014)
[14]
Huang, C., Li, J., Ross, K.W.: Can Internet Video-on-Demand be Protable. In: Proc. SIGCOMM (2007)
[15]
Lakkaraju, H., McAuley, J., Leskovec, J.: What’s in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media. In: Proc. ICWSM (2013)
[16]
Lerman, K., Hogg, T.: Using a Model of Social Dynamics to Predict Popularity of News. In: Proc. WWW (2010)
[17]
Li, H., Ma, X., Wang, F., Liu, J., Xu, K.: On popularity prediction of videos shared in online social networks. In: Proc. CIKM (2013)
[18]
Matsubara, Y., Sakurai, Y., Prakash, B.A., Li, L., Faloutsos, C.: Rise and Fall Patterns of Information Diffusion. In: Proc. KDD (2012)
[19]
Nannen, V.: A Short Introduction to Model Selection, Kolmogorov Complexity and Minimum Description Length (MDL). Complexity (Mdl), 20 (2010)
[20]
Pinto, H., Almeida, J., Gonçalves, M.: Using Early View Patterns to Predict the Popularity of YouTube Videos. In: Proc. WSDM (2013)
[21]
Radinsky K., Svore K., Dumais S., Teevan J., Bocharov A., and Horvitz E. Behavioral Dynamics on the Web: Learning, Modeling, and Prediction ACM Transactions on Information Systems 2013 32 3 1-37
[22]
Szabo G. and Huberman B.A. Predicting the Popularity of Online Content Communications of the ACM 2010 53 8 80-88
[23]
Vakali, A., Giatsoglou, M., Antaris, S.: Social networking trends and dynamics detection via a cloud-based framework design. In: Proc. WWW (2012)
[24]
Yang, J., Leskovec, J.: Patterns of temporal variation in online media. In: Proc. WSDM (2011)

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Published In

cover image Guide Proceedings
Machine Learning and Knowledge Discovery in Databases
748 pages
ISBN:978-3-662-44847-2
DOI:10.1007/978-3-662-44848-9
  • Editors:
  • Toon Calders,
  • Floriana Esposito,
  • Eyke Hüllermeier,
  • Rosa Meo

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 10 March 2022

Author Tags

  1. Root Mean Square Error
  2. Minimum Description Length
  3. Average Root Mean Square Error
  4. Minimum Description Length Principle
  5. Content Popularity

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