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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
We the people petitions from the Obama years are archived at https://petitions.obamawhitehouse.archives.gov.
- 2.
Our data are available at https://github.com/justinlai/petitiondata.
- 3.
For an alternative perspective on e-petition “success,” see Wright [47].
- 4.
- 5.
In footnote 10 on p. 187, Goel et al. clarify that this statement applies to normalized and not just to absolute size [14].
References
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)
Bandura, A., Cervone, D.: Differential engagement of self-reactive influences in cognitive motivation. Organ. Behav. Hum. Decis. Process. 38(1), 92–113 (1986)
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)
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)
Bass, F.M.: A new product growth for model consumer durables. Manag. Sci. 15(5), 215–227 (1969)
Centola, D., Macy, M.: Complex contagions and the weakness of long ties. Am. J. Sociol. 113(3), 702–734 (2007)
Chan, C.L.: Temporal dynamics of adoption and diffusion patterns in online petitioning. M.S. thesis, Stanford University (2015)
Cheema, A., Bagchi, R.: The effect of goal visualization on goal pursuit: implications for consumers and managers. J. Mark. 75(2), 109–123 (2011)
Coleman, J., Katz, E., Menzel, H.: The diffusion of an innovation among physicians. Sociometry 20, 253–270 (1957)
Cryder, C.E., Loewenstein, G., Seltman, H.: Goal gradient in helping behavior. J. Exp. Soc. Psychol. 49(6), 1078–1083 (2013)
Dodds, P.S., Watts, D.J.: A generalized model of social and biological contagion. J. Theor. Biol. 232(4), 587–604 (2005)
Gladwell, M.: The Tipping Point: How Little Things can Make a Big Difference. Little, Brown and Company, Boston (2002)
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
Goel, S., Anderson, A., Hofman, J., Watts, D.: The structural virality of online diffusion. Manag. Sci. 62(1), 180–196 (2016)
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)
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)
Gonzalez-Bailon, S., Borge-Holthoefer, J., Rivero, A., Moreno, Y.: The dynamics of protest recruitment through an online network. Sci. Rep. 1, 197 (2011)
Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420–1443 (1978)
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)
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)
Heath, C., Larrick, R.P., Wu, G.: Goals as reference points. Cogn. Psychol. 38(1), 79–109 (1999)
Hull, C.L.: The rat’s speed-of-locomotion gradient in the approach to food. J. Comp. Psychol. 17(3), 393 (1934)
Iyengar, R., Van Den Bulte, C., Valente, T.W.: Opinion leadership and social contagion in new product diffusion. Mark. Sci. 30, 195–212 (2011)
Jones, B.D., Baumgartner, F.R.: The Politics of Attention: How Government Prioritizes Problems. University of Chicago Press, Chicago (2005)
Jungherr, A., Jrgens, P.: The political click: political participation through E? Petitions in Germany. Policy Internet 2(4), 131–165 (2010)
Karpf, D.: Analytic Activism: Digital Listening and the New Political Strategy. Oxford University Press, Corby (2017)
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)
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)
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)
Koo, M., Fishbach, A.: The small-area hypothesis: effects of progress monitoring on goal adherence. J. Consum. Res. 39(3), 493–509 (2012)
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
Lin, Y.-R., Margolin, D., Keegan, B., Baronchelli, A., Lazer, D.: #bigbirds never die: understanding social dynamics of emergent hashtags (2013)
Locke, E.A.: Toward a theory of task motivation and incentives. Organ. Behav. Hum. Perform. 3(2), 157–189 (1968)
Lopez-Pintado, D., Watts, D.: Social influence, binary decisions and collective dynamics. Ration. Soc. 20(4), 399–443 (2008)
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)
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)
Rogers, E.: Diffusion of Innovations. Free Press, New York (1962)
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)
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)
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)
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)
Valente, T.W.: Network Models of the Diffusion of Innovations, vol. 2. Hampton Press, Cresskill (1995)
Van den Bulte, C., Lilien, G.L.: Medical innovation revisited: social contagion versus marketing effort. Am. J. Sociol. 106(5), 1409–1435 (2001)
Watts, D.J.: A simple model of global cascades on random networks. Proc. Nat. Acad. Sci. 99(9), 5766–5771 (2002)
Weng, L., Menczer, F., Ahn, Y.Y.: Predicting successful memes using network and community structure. In: ICWSM, March 2014
Wright, S.: E-petitions. In: Handbook of Digital Politics, p. 136 (2015). Chapter 9
Wright, S.: Success and online political participation: the case of Downing Street E-petitions. Inf. Commun. Soc. 19(6), 843–857 (2016)
Yasseri, T., Hale, S.A., Margetts, H.: Modeling the rise in internet-based petitions (2013). arXiv preprint arXiv:1308.0239
Young, H.P.: Innovation diffusion in heterogeneous populations: contagion, social influence, and social learning. Am. Econ. Rev. 99(5), 1899–1924 (2009)
Acknowledgements
We wish to thank Marek Hlavac for technical assistance, and Lee Ross and Howard Rheingold for timely and valuable feedback on an earlier version of this work (which was submitted by the first author as her masters thesis [7]), as well as three anonymous reviewers for their helpful comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendices
Appendix A: Signature Graphs for Individual Petitions
Appendix B: Aggregated Temporal Signature Graphs
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chan, C.L., Lai, J., Hooi, B., Davies, T. (2017). The Message or the Messenger? Inferring Virality and Diffusion Structure from Online Petition Signature Data. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10539. Springer, Cham. https://doi.org/10.1007/978-3-319-67217-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-67217-5_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67216-8
Online ISBN: 978-3-319-67217-5
eBook Packages: Computer ScienceComputer Science (R0)