The Message or the Messenger? Inferring Virality and Diffusion Structure from Online Petition Signature Data

  • Chi Ling Chan
  • Justin Lai
  • Bryan Hooi
  • Todd Davies
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10539)

Abstract

Goel et al. [14] examined diffusion data from Twitter to conclude that online petitions are shared more virally than other types of content. Their definition of structural virality, which measures the extent to which diffusion follows a broadcast model or is spread person to person (virally), depends on knowing the topology of the diffusion cascade. But often the diffusion structure cannot be observed directly. We examined time-stamped signature data from the Obama White House’s We the People petition platform. We developed measures based on temporal dynamics that, we argue, can be used to infer diffusion structure as well as the more intrinsic notion of virality sometimes known as infectiousness. These measures indicate that successful petitions are likely to be higher in both intrinsic and structural virality than unsuccessful petitions are. We also investigate threshold effects on petition signing that challenge simple contagion models, and report simulations for a theoretical model that are consistent with our data.

Keywords

Petitions Virality Broadcast Diffusion 

References

  1. 1.
    Adar, E., Adamic, L.: Tracking information epidemics in blogspace. In: IEEE/WIC/ACM International Conference on Web Intelligence. IEEE Computer Society, Compiegne University of Technology, France (2005)Google Scholar
  2. 2.
    Bandura, A., Cervone, D.: Differential engagement of self-reactive influences in cognitive motivation. Organ. Behav. Hum. Decis. Process. 38(1), 92–113 (1986)CrossRefGoogle Scholar
  3. 3.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 65–74. ACM (2011)Google Scholar
  4. 4.
    Bakshy, E., Karrer, B., Adamic, L.: Social influence and the diffusion of user-created content. In: Proceedings of the Tenth ACM Conference on Electronic Commerce, pp. 325–334. Association of Computing Machinery (2009)Google Scholar
  5. 5.
    Bass, F.M.: A new product growth for model consumer durables. Manag. Sci. 15(5), 215–227 (1969)CrossRefMATHGoogle Scholar
  6. 6.
    Centola, D., Macy, M.: Complex contagions and the weakness of long ties. Am. J. Sociol. 113(3), 702–734 (2007)CrossRefGoogle Scholar
  7. 7.
    Chan, C.L.: Temporal dynamics of adoption and diffusion patterns in online petitioning. M.S. thesis, Stanford University (2015)Google Scholar
  8. 8.
    Cheema, A., Bagchi, R.: The effect of goal visualization on goal pursuit: implications for consumers and managers. J. Mark. 75(2), 109–123 (2011)CrossRefGoogle Scholar
  9. 9.
    Coleman, J., Katz, E., Menzel, H.: The diffusion of an innovation among physicians. Sociometry 20, 253–270 (1957)CrossRefGoogle Scholar
  10. 10.
    Cryder, C.E., Loewenstein, G., Seltman, H.: Goal gradient in helping behavior. J. Exp. Soc. Psychol. 49(6), 1078–1083 (2013)CrossRefGoogle Scholar
  11. 11.
    Dodds, P.S., Watts, D.J.: A generalized model of social and biological contagion. J. Theor. Biol. 232(4), 587–604 (2005)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Gladwell, M.: The Tipping Point: How Little Things can Make a Big Difference. Little, Brown and Company, Boston (2002)Google Scholar
  13. 13.
    Gleeson, J.P., Cellai, D., Onnela, J.-P., Porter, M.A., Reed-Tsochas, F.: A simple generative model of collective online behaviour (2013). arXiv preprint arXiv:1305.7440
  14. 14.
    Goel, S., Anderson, A., Hofman, J., Watts, D.: The structural virality of online diffusion. Manag. Sci. 62(1), 180–196 (2016)Google Scholar
  15. 15.
    Goel, S., Watts, D.J., Goldstein, D.G.: The structure of online diffusion networks. In: Proceedings of the 13th ACM Conference on Electronic Commerce, pp. 623–638. ACM (2012)Google Scholar
  16. 16.
    Goldenberg, J., Libai, B., Muller, E.: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12(3), 211–223 (2001)CrossRefGoogle Scholar
  17. 17.
    Gonzalez-Bailon, S., Borge-Holthoefer, J., Rivero, A., Moreno, Y.: The dynamics of protest recruitment through an online network. Sci. Rep. 1, 197 (2011)CrossRefGoogle Scholar
  18. 18.
    Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420–1443 (1978)CrossRefGoogle Scholar
  19. 19.
    Hale, S.A., John, P., Margetts, H.Z., Yasseri, T.: Investigating political participation and social information using big data and a natural experiment. In: APSA 2014 Annual Meeting Paper (2014)Google Scholar
  20. 20.
    Hale, S.A., Margetts, H., Yasseri, T.: Petition growth and success rates on the UK no. 10 downing street website. In: Proceedings of the 5th Annual ACM Web Science Conference, pp. 132–138. ACM (2013)Google Scholar
  21. 21.
    Heath, C., Larrick, R.P., Wu, G.: Goals as reference points. Cogn. Psychol. 38(1), 79–109 (1999)CrossRefGoogle Scholar
  22. 22.
    Hull, C.L.: The rat’s speed-of-locomotion gradient in the approach to food. J. Comp. Psychol. 17(3), 393 (1934)CrossRefGoogle Scholar
  23. 23.
    Iyengar, R., Van Den Bulte, C., Valente, T.W.: Opinion leadership and social contagion in new product diffusion. Mark. Sci. 30, 195–212 (2011)CrossRefGoogle Scholar
  24. 24.
    Jones, B.D., Baumgartner, F.R.: The Politics of Attention: How Government Prioritizes Problems. University of Chicago Press, Chicago (2005)Google Scholar
  25. 25.
    Jungherr, A., Jrgens, P.: The political click: political participation through E? Petitions in Germany. Policy Internet 2(4), 131–165 (2010)CrossRefGoogle Scholar
  26. 26.
    Karpf, D.: Analytic Activism: Digital Listening and the New Political Strategy. Oxford University Press, Corby (2017)CrossRefGoogle Scholar
  27. 27.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association of Computing Machinery (2003)Google Scholar
  28. 28.
    Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)CrossRefGoogle Scholar
  29. 29.
    Kivetz, R., Urminsky, O., Zheng, Y.: The goal-gradient hypothesis resurrected: purchase acceleration, illusionary goal progress, and customer retention. J. Mark. Res. 43(1), 39–58 (2006)CrossRefGoogle Scholar
  30. 30.
    Koo, M., Fishbach, A.: The small-area hypothesis: effects of progress monitoring on goal adherence. J. Consum. Res. 39(3), 493–509 (2012)CrossRefGoogle Scholar
  31. 31.
    Leskovec, J., Singh, A., Kleinberg, J.: Patterns of influence in a recommendation network. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS, vol. 3918, pp. 380–389. Springer, Heidelberg (2006). doi:10.1007/11731139_44 CrossRefGoogle Scholar
  32. 32.
    Lin, Y.-R., Margolin, D., Keegan, B., Baronchelli, A., Lazer, D.: #bigbirds never die: understanding social dynamics of emergent hashtags (2013)Google Scholar
  33. 33.
    Locke, E.A.: Toward a theory of task motivation and incentives. Organ. Behav. Hum. Perform. 3(2), 157–189 (1968)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Lopez-Pintado, D., Watts, D.: Social influence, binary decisions and collective dynamics. Ration. Soc. 20(4), 399–443 (2008)CrossRefGoogle Scholar
  35. 35.
    Margetts, H.Z., John, P., Escher, T., Reissfelder, S.: Social information and political participation on the Internet: an experiment. Eur. Polit. Sci. Rev. 3(3), 321–344 (2011)CrossRefGoogle Scholar
  36. 36.
    Margetts, H.Z., John, P., Hale, S.A., Reissfelder, S.: Leadership without leaders? Starters and followers in online collective action. Polit. Stud. 63, 278–299 (2013)CrossRefGoogle Scholar
  37. 37.
    Rogers, E.: Diffusion of Innovations. Free Press, New York (1962)Google Scholar
  38. 38.
    Rothkopf, E.Z., Billington, M.J.: Goal-guided learning from text: inferring a descriptive processing model from inspection times and eye movements. J. Educ. Psychol. 71(3), 310 (1979)CrossRefGoogle Scholar
  39. 39.
    Salganik, M.J., Dodds, P.S., Watts, D.J.: Experimental study of inequality and unpredictability in an artificial cultural market. Science 311(5762), 854–856 (2006)CrossRefGoogle Scholar
  40. 40.
    Staab, S., Domingos, P., Golbeck, J., Ding, L., Finin, T., Joshi, A., Nowak, A.: Social networks applied. IEEE Intell. Syst. 20(1), 80–93 (2005)CrossRefGoogle Scholar
  41. 41.
    Sun, E., Rosenn, I., Marlow, C., Lento, T.: Gesundheit! modeling contagion through facebook news feed. In: Proceedings of International AAAI Conference on Weblogs and Social Media (2009)Google Scholar
  42. 42.
    Valente, T.W.: Network Models of the Diffusion of Innovations, vol. 2. Hampton Press, Cresskill (1995)Google Scholar
  43. 43.
    Van den Bulte, C., Lilien, G.L.: Medical innovation revisited: social contagion versus marketing effort. Am. J. Sociol. 106(5), 1409–1435 (2001)CrossRefGoogle Scholar
  44. 44.
    Watts, D.J.: A simple model of global cascades on random networks. Proc. Nat. Acad. Sci. 99(9), 5766–5771 (2002)MathSciNetCrossRefMATHGoogle Scholar
  45. 45.
    Weng, L., Menczer, F., Ahn, Y.Y.: Predicting successful memes using network and community structure. In: ICWSM, March 2014Google Scholar
  46. 46.
    Wright, S.: E-petitions. In: Handbook of Digital Politics, p. 136 (2015). Chapter 9Google Scholar
  47. 47.
    Wright, S.: Success and online political participation: the case of Downing Street E-petitions. Inf. Commun. Soc. 19(6), 843–857 (2016)CrossRefGoogle Scholar
  48. 48.
    Yasseri, T., Hale, S.A., Margetts, H.: Modeling the rise in internet-based petitions (2013). arXiv preprint arXiv:1308.0239
  49. 49.
    Young, H.P.: Innovation diffusion in heterogeneous populations: contagion, social influence, and social learning. Am. Econ. Rev. 99(5), 1899–1924 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Stanford UniversityStanfordUSA
  2. 2.Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations