Skip to main content

Advertisement

Log in

Exploring the effect of reinvention on critical mass formation and the diffusion of information in a social network

Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Widely used information diffusion models are based on the assumption that exposure to information, like exposure to a virus, is enough for it to spread. However, the diffusion of information is more complex and involves an array of decisions regarding whether to consume the information received, and upon consumption, whether to pass it on to others. Using an agent-based simulation run on an actual network graph, we model a realistic information consumption and transmission process. The model builds upon the combination of two prominent social theories: diffusion of innovations and critical mass theory. We introduce the concept of reinvention, the modification of information, and investigate its effect on both critical mass formation and on the overall diffusion of information in the social network. Results show that network topology is crucial while traditional concepts such as ‘inflection point’ or ‘early adopters’ are of secondary importance. Although reinvented information requires more nodes to reach critical mass, it prolongs the diffusion of information past the inflection point so that information reaches a larger final audience, while also exhibiting accelerated production functions. Reinvention is found to have a prominent effect when transmitted via weak ties, thus allowing information to undergo an evolutionary process that contributes to an overall higher value of information, collective outcome, in the network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  • Abiteboul S, Greenshpan O, Milo T (2008) Modeling the mashup space. In: Proceedings of the 10th ACM workshop on Web information and data management, pp 87–94

  • Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47

    Article  MATH  Google Scholar 

  • Ball P (2004) Critical mass. Farrar, Straus and Giroux, New York

    Google Scholar 

  • Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509

    Article  MathSciNet  Google Scholar 

  • Barabási A-L, Ravasz E, Vicsek T (2001) Deterministic scale-free networks. Phys A 299(3):559–564

    Article  MATH  Google Scholar 

  • Barrot C, Albers S (2008) Did they tell their friends? Using social network analysis to detect contagion processes. Available at SSRN: http://ssrn.com/abstract=1091205

  • Berger JA, Heath C (2005) Idea habitats: how the prevalence of environmental cues influences the success of ideas. Cogn Sci 29(2):195–221

    Article  Google Scholar 

  • Berger JA, Milkman KL (2009) Social transmission, emotion, and the virality of online content. Wharton Research Paper

  • Berman P, McLaughlin MW (1975) Federal programs supporting educational change, vol IV: the findings in review

  • Blakely CH, Mayer JP, Gottschalk RG, Schmitt N, Davidson WS, Roitman DB, Emshoff JG (1987) The fidelity-adaptation debate: implications for the implementation of public sector social programs. Am J Community Psychol 15(3):253–268

    Article  Google Scholar 

  • Burt, R. S. (1997). The contingent value of social capital. Administrative science quarterly, 339–365

  • Canright GS, Engø-Monsen K (2006) Spreading on networks: a topographic view. Complexus 3(1):131–146

    Article  Google Scholar 

  • Dezső Z, Barabási A-L (2002) Halting viruses in scale-free networks. Physical Review E, 65(5), 055103

    Google Scholar 

  • Dodds PS, Muhamad R, Watts DJ (2003) An experimental study of search in global social networks. Science 301(5634):827–829

    Article  Google Scholar 

  • Fulk J, Heino R, Flanagin AJ, Monge PR, Bar F (2004) A test of the individual action model for organizational information commons. Organ Sci 15(5):569–585

    Article  Google Scholar 

  • Goel S, Watts DJ, Goldstein DG (2012) The structure of online diffusion networks. In: Proceedings of the 13th ACM conference on electronic commerce, pp 623–638. http://dl.acm.org/citation.cfm?id=2229058

  • Goldenberg J, Efroni S (2001) Using cellular automata modeling of the emergence of innovations. Technol Forecast Soc Chang 68(3):293–308

    Article  Google Scholar 

  • Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. In: Proceedings of the 13th international conference on World Wide Web, pp 491–501

  • Havelock RG (1974) Locals say innovation is local: a national survey of school superintendents. In: What do research findings say about getting innovations into schools: a symposium. Research for Better Schools, Philadelphia

  • Howard-Spink S (2005) Grey Tuesday, online cultural activism and the mash-up of music and politics (originally published in October 2004). First Monday

  • Jenkins H (2009) Confronting the challenges of participatory culture: media education for the 21st century. The MIT Press, Boston

    Google Scholar 

  • Kelly JA, Heckman TG, Stevenson LY, Williams PN, Ertl T, Hays RB, Neumann MS (2000) Transfer of research-based HIV prevention interventions to community service providers: fidelity and adaptation. AIDS Educ Prev Off Publ Int Soc AIDS Educ 12(5 Suppl):87–98

    Google Scholar 

  • Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, pp 137–146. ACM, Washington, D.C

  • Kermark M, Mckendrick A (1927) Contributions to the mathematical theory of epidemics. Part I. Proc Roy Soc A 115:700–721

    Article  Google Scholar 

  • Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888–893

    Article  Google Scholar 

  • Kossinets G, Watts DJ (2006) Empirical analysis of an evolving social network. Science 311(5757):88–90

    Article  MathSciNet  MATH  Google Scholar 

  • Lazarsfeld PF (1944) Communications research, vol 2. Harper, New York

    Google Scholar 

  • Lazarsfeld PF, Menzel H et al (1961) On the relation between individual and collective properties. In: Complex organizations, vol 1, pp 422–440

  • Mamman A (2002) The adoption and modification of management ideas in organizations: towards an analytical framework. Strateg Chang 11(7):379–389

    Article  Google Scholar 

  • Markus ML (1987) Toward a “critical mass” theory of interactive media. Commun Res 14(5):491–511. doi:10.1177/009365087014005003

    Article  Google Scholar 

  • Marwell G, Oliver PE, Prahl R (1988) Social networks and collective action: a theory of the critical mass. III. Am J Sociol 94(3):502–534

    Article  Google Scholar 

  • Moldovan S, Goldenberg J (2004) Cellular automata modeling of resistance to innovations: effects and solutions. Technol Forecast Soc Chang 71(5):425–442

    Article  Google Scholar 

  • Monge PR, Fulk J, Kalman ME, Flanagin AJ, Parnassa C, Rumsey S (1998) Production of collective action in alliance-based interorganizational communication and information systems. Organ Sci 9(3):411–433

    Article  Google Scholar 

  • Oliver PE, Marwell G (2001) Whatever happened to critical mass theory? A retrospective and assessment. Sociol Theory 19(3):292–311

    Article  Google Scholar 

  • Oliver P, Marwell G, Teixeira R (1985) A theory of the critical mass. I. Interdependence, group heterogeneity, and the production of collective action. Am J Sociol 91(3):522–556

    Article  Google Scholar 

  • Pastor-Satorras R, Vespignani A (2001) Epidemic spreading in scale-free networks. Phys Rev Lett 86(14):3200–3203

    Article  Google Scholar 

  • Peddibhotla NB, Subramani MR (2007) Contributing to public document repositories: a critical mass theory perspective. Organ Stud 28(3):327

    Article  Google Scholar 

  • Rafaeli S, Larose RJ (1993) Electronic bulletin boards and “public goods” explanations of collaborative mass media. Commun Res 20(2):277–297. doi:10.1177/009365093020002005

    Article  Google Scholar 

  • Rogers EM (1962) Diffusion of innovations. Free Press, Glencoe

    Google Scholar 

  • Rogers EM (1995) Diffusion of innovations. Simon and Schuster, New York

    Google Scholar 

  • Rogers EM (2003) Diffusion of innovations. Free Press, New York

    Google Scholar 

  • Romero DM, Meeder B, Kleinberg J (2011) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter. In: Proceedings of the 20th international conference on World Wide Web, pp 695–704

  • Schelling TC (1978) Micromotives and macrobehavior. WW Norton & Company, New York

    Google Scholar 

  • Shakarian P, Eyre S, Paulo D (2013) A scalable heuristic for viral marketing under the tipping model. Soc Netw Anal Min 3(4):1225–1248

    Google Scholar 

  • Singhal A, Dearing JW (2006) Communication of innovations: a journey with Ev Rogers. Sage Publications, USA

    Book  Google Scholar 

  • Stephen AT, Dover Y, Goldenberg J (2010) A comparison of the effects of transmitter activity and connectivity on the diffusion of information over online social networks. SSRN eLibrary. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1609611

  • Valente TW (1996) Social network thresholds in the diffusion of innovations. Soc Netw 18(1):69–89

    Article  MathSciNet  Google Scholar 

  • Von Hippel E (1976) The dominant role of users in the scientific instrument innovation process. Res Policy 5(3):212–239. doi:10.1016/0048-7333(76)90028-7

    Article  Google Scholar 

  • Watts Duncan J (2002) A simple model of global cascades on random networks. Proc Natl Acad Sci 99(9):5766–5771

    Article  MathSciNet  MATH  Google Scholar 

  • Watts DJ, Dodds P (2007) The accidental influentials. Harv Bud Rev 85(2):22–23

    Google Scholar 

  • Wojnicki A, Godes D (2008) Word-of-mouth as self-enhancement. HBS Marketing Research Paper No. 06-01

  • Wu F, Huberman BA, Adamic LA, Tyler JR (2004) Information flow in social groups. Phys A 337(1):327–335

    Article  MathSciNet  Google Scholar 

  • Yew J (2009) Social performances: understanding the motivations for online participatory behavior. In: Proceedings of the ACM 2009 international conference on supporting group work, pp 397–398

  • Zhao L, Wang J, Chen Y, Wang Q, Cheng J, Cui H (2012) SIHR rumor spreading model in social networks. Phys A Stat Mech Appl 391(7):995–1003

    Article  Google Scholar 

Download references

Acknowledgments

Partial funding was received from the EC project SocIoS, FP 7 STREP Project No. 257774. Additional funding was received from the I-CORE Program of the Planning and Budgeting Committee and The Israel Science Foundation (Grant No. 1716/12).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daphne R. Raban.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Koren, H., Kaminer, I. & Raban, D.R. Exploring the effect of reinvention on critical mass formation and the diffusion of information in a social network. Soc. Netw. Anal. Min. 4, 185 (2014). https://doi.org/10.1007/s13278-014-0185-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-014-0185-5

Keywords

Navigation