skip to main content
10.1145/2566486.2567984acmotherconferencesArticle/Chapter ViewAbstractPublication PageswwwConference Proceedingsconference-collections
research-article

Modeling and predicting the growth and death of membership-based websites

Published:07 April 2014Publication History

ABSTRACT

Driven by outstanding success stories of Internet startups such as Facebook and The Huffington Post, recent studies have thoroughly described their growth. These highly visible online success stories, however, overshadow an untold number of similar ventures that fail. The study of website popularity is ultimately incomplete without general mechanisms that can describe both successes and failures. In this work we present six years of the daily number of users (DAU) of twenty-two membership-based websites - encompassing online social networks, grassroots movements, online forums, and membership-only Internet stores - well balanced between successes and failures. We then propose a combination of reaction-diffusion-decay processes whose resulting equations seem not only to describe well the observed DAU time series but also provide means to roughly predict their evolution. This model allows an approximate automatic DAU-based classification of websites into self-sustainable v.s. unsustainable and whether the startup growth is mostly driven by marketing & media campaigns or word-of-mouth adoptions.

References

  1. Alexa.com. http://www.alexa.com/help/traffic-learn-more.Google ScholarGoogle Scholar
  2. W. B. Arthur. Increasing Returns and Path Dependence in the Economy. U. Michigan Press, 1994.Google ScholarGoogle ScholarCross RefCross Ref
  3. Lars Backstrom, Eytan Bakshy, Jon M Kleinberg, Thomas M Lento, and Itamar Rosenn. Center of attention: How facebook users allocate attention across friends. ICWSM, 2011.Google ScholarGoogle Scholar
  4. Lars Backstrom, Dan Huttenlocher, Jon Kleinberg, and Xiangyang Lan. Group formation in large social networks: membership, growth, and evolution. In SIGKDD, pages 44--54, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Venkatesh Bala and Sanjeev Goyal. A Noncooperative Model of Network Formation. Econometrica, 68(5):1181--1229, September 2000.Google ScholarGoogle ScholarCross RefCross Ref
  6. Frank Bass. Comments on "A New Product Growth for Model Consumer Durables The Bass Model". Management science, 50(12 supplement):1833--1840, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Danah Boyd. White flight in networked publics? how race and class shaped american teen engagement with myspace and facebook. In Lisa Nakamura, Peter Chow-White, and Alondra Nelson, editors, Race After the Internet, pages 203--222. Routledge, 2011.Google ScholarGoogle Scholar
  8. Tadeusz Cali'nski and Jerzy Harabasz. A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1):1--27, 1974.Google ScholarGoogle ScholarCross RefCross Ref
  9. Peter Cauwels and Didier Sornette. Quis pendit ipsa pretia: Facebook valuation and diagnostic of a bubble based on nonlinear demographic dynamics. The Journal of Portfolio Management, 38(2):56--66, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  10. Damon Centola. The spread of behavior in an online social network experiment. Science, 329(5996):1194--1197, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  11. Nicholas Christakis and James Fowler. The Spread of Obesity in a Large Social Network over 32 Years. New England Journal of Medicine, 357(4):370--379, July 2007.Google ScholarGoogle ScholarCross RefCross Ref
  12. Vittoria Colizza, Romualdo Pastor-Satorras, and Alessandro Vespignani. Reaction--diffusion processes and metapopulation models in heterogeneous networks. Nature Physics, 3(4):276--282, March 2007.Google ScholarGoogle ScholarCross RefCross Ref
  13. Vittoria Colizza and Alessandro Vespignani. Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: Theory and simulations. Journal of Theoretical Biology, 251(3):450--467, April 2008.Google ScholarGoogle ScholarCross RefCross Ref
  14. R Crane and D Sornette. Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences, 105(41):15649--15653, October 2008.Google ScholarGoogle ScholarCross RefCross Ref
  15. Crunchbase. http://www.crunchbase.com.Google ScholarGoogle Scholar
  16. Z. Dezsö, E. Almaas, A. Lukács, B. Rácz, I. Szakadát, and A. L. Barabasi. Dynamics of information access on the web. Physical Review E, 73(6):066132, June 2006.Google ScholarGoogle ScholarCross RefCross Ref
  17. Joseph Farrell and Paul Klemperer. Coordination and lock-in: Competition with switching costs and network effects. Handbook of industrial organization, 3:1967--2072, 2007.Google ScholarGoogle Scholar
  18. John C. Fisher and Robert H. Pry. A simple substitution model of technological change. Technological forecasting and social change, 3:75--88, 1972.Google ScholarGoogle Scholar
  19. Daniel Friedman. Evolutionary Games in Economics. Econometrica, 59(3):637--666, January 1991.Google ScholarGoogle ScholarCross RefCross Ref
  20. David Garcia, Pavlin Mavrodiev, and Frank Schweitzer. Social Resilience in Online Communities: The Autopsy of Friendster. In Proc.\ of COSN, pages 39--50, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Paul Geroski. Models of technology diffusion. Research policy, 29(4):603--625, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  22. Oana Goga, Daniele Perito, Howard Lei, Renata Teixeira, and Robin Sommer. Large-scale Correlation of Accounts Across Social Networks. Technical report, 2013.Google ScholarGoogle Scholar
  23. Mark Granovetter. Threshold models of collective behavior. American journal of sociology, pages 1420--1443, 1978.Google ScholarGoogle Scholar
  24. Richard H. Smith Historian and Political Consultant Richard H. Smith. Why does Google News ignore The Huffington Post-http://goo.gl/rom0mg.Google ScholarGoogle Scholar
  25. Facebook Inc. Facebook second quarter 2013 results: http://investor.fb.com/releasedetail.cfm?ReleaseID=780093.Google ScholarGoogle Scholar
  26. Matthew O. Jackson. A survey of network formation models: stability and efficiency. Group formation in economics: networks, clubs and coalitions. Cambridge University Press, Cambridge, pages 11--57, 2005.Google ScholarGoogle Scholar
  27. Anick Jesdanun. MySpace popularity with teens fizzles, MSNBC News, Nov. 2007.Google ScholarGoogle Scholar
  28. Emily Jin, Michelle Girvan, and M Newman. Structure of growing social networks. Physical Review E, 64(4):046132, September 2001.Google ScholarGoogle ScholarCross RefCross Ref
  29. Sanjay Ram Kairam, Dan J Wang, and Jure Leskovec. The life and death of online groups: Predicting group growth and longevity. In WSDM, pages 673--682, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. E. L. Kaplan and Paul Meier. Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282):457--481, June 1958.Google ScholarGoogle ScholarCross RefCross Ref
  31. Michael Katz and Carl Shapiro. Network externalities, competition, and compatibility. The American economic review, 75(3):424--440, 1985.Google ScholarGoogle Scholar
  32. Ravi Kumar, Jasmine Novak, and Andrew Tomkins. Structure and Evolution of Online Social Networks. In link.springer.com, pages 337--357. Springer New York, New York, NY, August 2010.Google ScholarGoogle Scholar
  33. David Lazer, Alex Sandy Pentland, Lada Adamic, Sinan Aral, Albert Laszlo Barabasi, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, Tony Jebara, Gary King, Michael Macy, Deb Roy, and Marshall Van Alstyne. Life in the network: the coming age of computational social science. Science, 323(5915):721, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  34. Jure Leskovec, Lars Backstrom, Ravi Kumar, and Andrew Tomkins. Microscopic evolution of social networks. In SIGKDD, pages 462--470, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. Graph evolution: Densification and shrinking diameters. TKDD, 1(1):2, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Kenneth Levenberg. A method for the solution of certain non-linear problems in least squares. Quarterly Journal of Applied Mathmatics, II(2):164--168, 1944.Google ScholarGoogle ScholarCross RefCross Ref
  37. Stan J Liebowitz and Stephen E Margolis. Network externality: An uncommon tragedy. The Journal of Economic Perspectives, 8(2):133--150, 1994.Google ScholarGoogle ScholarCross RefCross Ref
  38. Edwin Mansfield. Technical change and the rate of imitation. Econometrica: Journal of the Econometric Society, pages 741--766, 1961.Google ScholarGoogle ScholarCross RefCross Ref
  39. Edwin Mansfield. Intrafirm Rates of Diffusion of an Innovation. The Review of Economics and Statistics, 45(4):348--359, January 1963.Google ScholarGoogle ScholarCross RefCross Ref
  40. M. Marsili. The rise and fall of a networked society: A formal model. Proceedings of the National Academy of Sciences, 101(6):1439--1442, January 2004.Google ScholarGoogle ScholarCross RefCross Ref
  41. A. Montanari and A. Saberi. The spread of innovations in social networks. Proceedings of the National Academy of Sciences, 107(47):20196--20201, November 2010.Google ScholarGoogle ScholarCross RefCross Ref
  42. James Dickson Murray. Mathematical biology, volume 2. springer, 2002.Google ScholarGoogle Scholar
  43. Bruno Ribeiro. Modeling the relationship between social network activity, inactivity, and growth. arXiv:1307.1354v2.Google ScholarGoogle Scholar
  44. Bruno Ribeiro, William Gauvin, Benyuan Liu, and Don Towsley. On myspace account spans and double pareto-like distribution of friends. In INFOCOM NetSciCom, pages 1--6, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  45. M. Rogers Everett. Diffusion of innovations. Free Press, 5th edition, 2003.Google ScholarGoogle Scholar
  46. Thomas C. Schelling. Micromotives and macrobehavior. WW Norton & Company, 2006.Google ScholarGoogle Scholar
  47. US SEC. Sec filing for fundrasing of theblaze.com:http://goo.gl/umgyuA.Google ScholarGoogle Scholar
  48. Herbert A. Simon. Designing Organizations for an Information-Rich World. In Martin Greenberger, editor, Computers, communications, and the public interest, pages 37--72. Johns Hopkins University Press, 1971.Google ScholarGoogle Scholar
  49. Brian Skyrms and Robin Pemantle. A Dynamic Model of Social Network Formation. In scholar.google.com, pages 231--251. Springer Berlin Heidelberg, Berlin, Heidelberg, July 2009.Google ScholarGoogle Scholar
  50. Jennifer Slegg. jennifer-slegg Patch.com, Huffington Post dominate Google News results:http://goo.gl/SK3GH.Google ScholarGoogle Scholar
  51. Tom A B Snijders. Stochastic actor-oriented models for network change. The Journal of Mathematical Sociology, 21(1--2):149--172, April 1996.Google ScholarGoogle ScholarCross RefCross Ref
  52. David Strang and Sarah A Soule. Diffusion in organizations and social movements: From hybrid corn to poison pills. Annual review of sociology, pages 265--290, 1998.Google ScholarGoogle Scholar
  53. Gabor Szabo and Bernardo A Huberman. Predicting the popularity of online content. Communications of the ACM, 53(8):80, August 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. TechCrunch Deadpool. http://techcrunch.com/tag/deadpool/.Google ScholarGoogle Scholar
  55. Mojtaba Torkjazi, Reza Rejaie, and Walter Willinger. Hot today, gone tomorrow. In WOSN, page 43, New York, New York, USA, 2009. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Johan Ugander, Lars Backstrom, Cameron Marlow, and Jon Kleinberg. Structural diversity in social contagion. PNAS, 109(16):5962--5966, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  57. N. G. van Kampen. Stochastic processes in physics and chemistry. North-Holland, 1, 1981.Google ScholarGoogle Scholar
  58. F. van Wijland, K. Oerding, and H. J. Hilhorst. Wilson renormalization of a reaction--diffusion process. Physica A: Statistical Mechanics and its Applications, 251(1--2):179--201, March 1998.Google ScholarGoogle Scholar
  59. Wikimedia Foundation. http://en.wikipedia.org/wiki/List\_of\_social\_networking\_websites.Google ScholarGoogle Scholar
  60. F Wu and B A Huberman. Novelty and collective attention. Proceedings of the National Academy of Sciences, 104(45):17599--17601, October 2007.Google ScholarGoogle ScholarCross RefCross Ref
  61. H. Young. The dynamics of social innovation. Proceedings of the National Academy of Sciences, 108(4):21285--21291, December 2011.Google ScholarGoogle ScholarCross RefCross Ref
  62. Xiaohan Zhao, Alessandra Sala, Christo Wilson, Xiao Wang, Sabrina Gaito, Haitao Zheng, and Ben Y Zhao. Multi-scale dynamics in a massive online social network. In IMC, pages 171--184, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Modeling and predicting the growth and death of membership-based websites

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        WWW '14: Proceedings of the 23rd international conference on World wide web
        April 2014
        926 pages
        ISBN:9781450327442
        DOI:10.1145/2566486

        Copyright © 2014 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 7 April 2014

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        WWW '14 Paper Acceptance Rate84of645submissions,13%Overall Acceptance Rate1,899of8,196submissions,23%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader