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Influentials, Networks, and Public Opinion Formation

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  • Duncan J. Watts
  • Peter Sheridan Dodds
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    Abstract

    A central idea in marketing and diffusion research is that influentials-a minority of individuals who influence an exceptional number of their peers-are important to the formation of public opinion. Here we examine this idea, which we call the "influentials hypothesis," using a series of computer simulations of interpersonal influence processes. Under most conditions that we consider, we find that large cascades of influence are driven not by influentials but by a critical mass of easily influenced individuals. Although our results do not exclude the possibility that influentials can be important, they suggest that the influentials hypothesis requires more careful specification and testing than it has received. (c) 2007 by JOURNAL OF CONSUMER RESEARCH, Inc..

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    File URL: http://www.journals.uchicago.edu/doi/pdf/10.1086/518527
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    Bibliographic Info

    Article provided by University of Chicago Press in its journal Journal of Consumer Research.

    Volume (Year): 34 (2007)
    Issue (Month): 4 (05)
    Pages: 441-458

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    Handle: RePEc:ucp:jconrs:v:34:y:2007:i:4:p:441-458

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    Web page: http://www.journals.uchicago.edu/JCR/

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    Cited by:
    1. Rubin, Jared, 2011. "Centralized institutions and cascades," MPRA Paper 32364, University Library of Munich, Germany.
    2. Pan, Zhengzheng, 2010. "Trust, influence, and convergence of behavior in social networks," Mathematical Social Sciences, Elsevier, vol. 60(1), pages 69-78, July.
    3. Jakub Steiner & Colin Stewart, 2010. "Influential Opinion Leaders," Working Papers tecipa-403, University of Toronto, Department of Economics.
    4. López-Pintado, Dunia, 2012. "Influence networks," Games and Economic Behavior, Elsevier, vol. 75(2), pages 776-787.
    5. Florian Probst & Laura Grosswiele & Regina Pfleger, 2013. "Who will lead and who will follow: Identifying Influential Users in Online Social Networks," Business & Information Systems Engineering, Springer, vol. 5(3), pages 179-193, June.
    6. H Peyton Young & Gabriel E. Kreindler, 2012. "Rapid Innovation Diffusion in Social Networks," Economics Series Working Papers 626, University of Oxford, Department of Economics.
    7. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics, Springer, vol. 10(1), pages 111-150, March.
    8. Amini, Mehdi & Wakolbinger, Tina & Racer, Michael & Nejad, Mohammad G., 2012. "Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach," European Journal of Operational Research, Elsevier, vol. 216(2), pages 301-311.
    9. Jeehong Kim & Wonchang Hur, 2013. "Diffusion of competing innovations in influence networks," Journal of Economic Interaction and Coordination, Springer, vol. 8(1), pages 109-124, April.
    10. Vincent Labatut & Jean-Michel Balasque, 2012. "Detection and Interpretation of Communities in Complex Networks: Methods and Practical Application," Post-Print hal-00633653, HAL.
    11. H Peyton Young & Gabriel E. Kreindler, 2011. "Fast Convergence in Evolutionary Equilibrium Selection," Economics Series Working Papers 569, University of Oxford, Department of Economics.
    12. Rodolfo Baggio, 2011. "The Mechanism for Spreading Online Reputation," Academica Turistica - Tourism and Innovation Journal, University of Primorska, Faculty of Tourism Studies - Turistica,, vol. 4(2), pages 5-13.
    13. Gentina, Elodie & Butori, Raphaëlle & Heath, Timothy B., 2014. "Unique but integrated: The role of individuation and assimilation processes in teen opinion leadership," Journal of Business Research, Elsevier, vol. 67(2), pages 83-91.
    14. Neilson, William & Wichmann, Bruno, 2014. "Social networks and non-market valuations," Journal of Environmental Economics and Management, Elsevier, vol. 67(2), pages 155-170.
    15. Passos, Gisèle, 2013. "La vengeance du consommateur insatisfait sur Internet et l'effet sur les attitudes des autres consommateurs," Economics Thesis from University Paris Dauphine, Paris Dauphine University, number 123456789/11827 edited by Le Nagard, Emmanuelle.
    16. Hinz, Oliver & Schulze, Christian & Takac, Carsten, 2014. "New product adoption in social networks: Why direction matters," Journal of Business Research, Elsevier, vol. 67(1), pages 2836-2844.
    17. Ding, Fei & Liu, Yun & Shen, Bo & Si, Xia-Meng, 2010. "An evolutionary game theory model of binary opinion formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1745-1752.
    18. Vincent Labatut & Jean-Michel Balasque, 2010. "Business-oriented Analysis of a Social Network of University Students," Post-Print hal-00633643, HAL.
    19. Kreindler, Gabriel E. & Young, H. Peyton, 2013. "Fast convergence in evolutionary equilibrium selection," Games and Economic Behavior, Elsevier, vol. 80(C), pages 39-67.
    20. Bentley, R. Alexander & Madsen, Mark E. & Ormerod, Paul, 2009. "Physical space and long-tail markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(5), pages 691-696.
    21. Lee, Seung Hwan (Mark), 2014. "The role of consumers' network positions on information-seeking behavior of experts and novices: A power perspective," Journal of Business Research, Elsevier, vol. 67(1), pages 2853-2859.

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