IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0286369.html
   My bibliography  Save this article

Who are the key players? Listeners vs spreaders vs others

Author

Listed:
  • Sumin Kim
  • Kyu-Min Lee
  • Euncheol Shin

Abstract

The literature on social learning examines how people learn from their neighbors and reach a consensus. The DeGroot social learning model describes the social learning process as one in which people form their opinions by taking a weighted average of their neighbors’ opinions. In the model, the influence structure is represented by a single matrix. In this paper, we empirically identify the role of the hub and authority centralities based on this matrix using data on microfinance adoption in rural Indian villages. Controlling for other well-known centrality measures, authority centrality is positively associated with final adoption rates in the villages, but hub centrality is not. Furthermore, we find that authority centrality is the most informative variable predicting microfinance diffusion success from LASSO regressions.

Suggested Citation

  • Sumin Kim & Kyu-Min Lee & Euncheol Shin, 2023. "Who are the key players? Listeners vs spreaders vs others," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-14, May.
  • Handle: RePEc:plo:pone00:0286369
    DOI: 10.1371/journal.pone.0286369
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286369
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0286369&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0286369?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lori Beaman & Ariel BenYishay & Jeremy Magruder & Ahmed Mushfiq Mobarak, 2021. "Can Network Theory-Based Targeting Increase Technology Adoption?," American Economic Review, American Economic Association, vol. 111(6), pages 1918-1943, June.
    2. Choi, S. & Goyal, S. & Moisan, F. & To, Y. Y. T., 2022. "Learning in Canonical Networks," Janeway Institute Working Papers 2212, Faculty of Economics, University of Cambridge.
    3. Pogorelskiy. Kirill & Shum, Matthew, 2019. "News We Like to Share : How News Sharing on Social Networks Influences Voting Outcomes," The Warwick Economics Research Paper Series (TWERPS) 1199, University of Warwick, Department of Economics.
    4. repec:bla:jfinan:v:59:y:2004:i:1:p:137-163 is not listed on IDEAS
    5. Abhijit Banerjee & Esther Duflo & Rachel Glennerster & Cynthia Kinnan, 2015. "The Miracle of Microfinance? Evidence from a Randomized Evaluation," American Economic Journal: Applied Economics, American Economic Association, vol. 7(1), pages 22-53, January.
    6. Pogorelskiy, Kirill & Shum, Matthew, 2019. "News We Like to Share: How News Sharing on Social Networks Influences Voting Outcomes," CAGE Online Working Paper Series 427, Competitive Advantage in the Global Economy (CAGE).
    7. Arun G. Chandrasekhar & Horacio Larreguy & Juan Pablo Xandri, 2020. "Testing Models of Social Learning on Networks: Evidence From Two Experiments," Econometrica, Econometric Society, vol. 88(1), pages 1-32, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wenhao Cheng, 2024. "Naïve learning as a coordination device in social networks," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 26(3), June.
    2. Felix Chopra & Ingar K. Haaland & Christopher Roth, 2019. "Do People Value More Informative News?," CESifo Working Paper Series 8026, CESifo.
    3. Fabrizio Germano & Vicenç Gómez & Francesco Sobbrio, 2022. "Ranking for Engagement: How Social Media Algorithms Fuel Misinformation and Polarization," CESifo Working Paper Series 10011, CESifo.
    4. van Gils, Freek & Müller, Wieland & Prüfer, Jens, 2020. "Big Data and Democracy," Other publications TiSEM ecc11d8d-1478-4dd2-b570-4, Tilburg University, School of Economics and Management.
    5. Marco Battaglini & Rebecca B. Morton & Eleonora Patacchini, 2020. "Social Groups and the Effectiveness of Protests," Working Papers 20200039, New York University Abu Dhabi, Department of Social Science, revised Feb 2020.
    6. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    7. Jacob Meyer & Prithvijit Mukherjee & Lucas Rentschler, 2024. "Moderating (mis)information," Public Choice, Springer, vol. 199(1), pages 159-186, April.
    8. Giacomo De Luca & Thilo R. Huning & Paulo Santos Monteiro, 2021. "Britain has had enough of experts? Social networks and the Brexit referendum," Discussion Papers 21/01, Department of Economics, University of York.
    9. Ayesha Ali & Ihsan Ayyub Qazi, 2021. "Countering Misinformation on Social Media Through Educational Interventions: Evidence from a Randomized Experiment in Pakistan," Papers 2107.02775, arXiv.org.
    10. Buechel, Berno & Mechtenberg, Lydia, 2019. "The swing voter's curse in social networks," Games and Economic Behavior, Elsevier, vol. 118(C), pages 241-268.
    11. Cátia Batista & Marcel Fafchamps & Pedro C Vicente, 2022. "Keep It Simple: A Field Experiment on Information Sharing among Strangers [Changing Saving and Investment Behavior: The Impact of Financial Literacy Training and Reminders on Micro-Businesses]," The World Bank Economic Review, World Bank, vol. 36(4), pages 857-888.
    12. Ali, Ayesha & Qazi, Ihsan Ayyub, 2023. "Countering misinformation on social media through educational interventions: Evidence from a randomized experiment in Pakistan," Journal of Development Economics, Elsevier, vol. 163(C).
    13. de Janvry, Alain & Sadoulet, Elisabeth, 2020. "Using agriculture for development: Supply- and demand-side approaches," World Development, Elsevier, vol. 133(C).
    14. Bowen, T. Renee & Galperti, Simone & Dmitriev, Danil, 2021. "Learning from Shared News: When Abundant Information Leads to Belief Polarization," CEPR Discussion Papers 15789, C.E.P.R. Discussion Papers.
    15. Kazushi Takahashi & Yukichi Mano & Keijiro Otsuka, 2018. "Spillovers as a Driver to Reduce Ex-post Inequality Generated by Randomized Experiments: Evidence from an Agricultural Training Intervention," Working Papers 174, JICA Research Institute.
    16. Chowdhury, Shyamal & Smits, Joeri & Sun, Qigang, 2020. "Contract structure, time preference, and technology adoption," GLO Discussion Paper Series 633, Global Labor Organization (GLO).
    17. Joan Calzada & Nestor Duch-Brown & Ricard Gil, 2021. "Do search engines increase concentration in media markets?," UB School of Economics Working Papers 2021/415, University of Barcelona School of Economics.
    18. Budzinski, Oliver & Gänßle, Sophia & Lindstädt-Dreusicke, Nadine, 2021. "Data (r)evolution - The economics of algorithmic search and recommender services," Ilmenau Economics Discussion Papers 148, Ilmenau University of Technology, Institute of Economics.
    19. Raúl Duarte & Frederico Finan & Horacio Larreguy & Laura Schechter, 2019. "Brokering Votes With Information Spread Via Social Networks," NBER Working Papers 26241, National Bureau of Economic Research, Inc.
    20. de Janvry, Alain & Sadoulet, Elisabeth, 2020. "Using agriculture for development: Supply- and demand-side approaches," World Development, Elsevier, vol. 133(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0286369. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.