IDEAS home Printed from https://ideas.repec.org/p/cam/camjip/2202.html
   My bibliography  Save this paper

In platforms we trust: misinformation on social networks in the presence of social mistrust

Author

Listed:
  • Charlson, G.

Abstract

We examine the effect social mistrust has on the propagation of misinformation on a social network. Agents communicate with each other and observe information sources, changing their opinion with some probability determined by their social trust, which can be low or high. Low social trust agents are less likely to be convinced out of their opinion by their peers and, in line with recent empirical literature, are more likely to observe misinformative information sources. A platform facilitates the creation of a homophilic network where users are more likely to connect with agents of the same level of social trust and the same social characteristics. Networks in which worldview is relatively important in determining network structure have more pronounced echo chambers, reducing the extent to which high and low social trust agents interact. Due to the asymmetric nature of these interactions, echo chambers then decrease the probability that agents believe misinformation. At the same time, they increase polarisation, as disagreeing agents interact less frequently, leading to a trade-off which has implications for the optimal intervention of a platform wishing to reduce misinformation. We characterise this intervention by delineating the most effective change in the platform's algorithm, which for peer-to-peer connections involves reducing the extent to which relatively isolated high and low social trust agents interact with one another.

Suggested Citation

  • Charlson, G., 2022. "In platforms we trust: misinformation on social networks in the presence of social mistrust," Janeway Institute Working Papers 2202, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camjip:2202
    Note: gc556
    as

    Download full text from publisher

    File URL: https://www.janeway.econ.cam.ac.uk/working-paper-pdfs/jiwp2202.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li Chen & Yiangos Papanastasiou, 2021. "Seeding the Herd: Pricing and Welfare Effects of Social Learning Manipulation," Management Science, INFORMS, vol. 67(11), pages 6734-6750, November.
    2. Jerry Anunrojwong & Krishnamurthy Iyer & Vahideh Manshadi, 2023. "Information Design for Congested Social Services: Optimal Need-Based Persuasion," Management Science, INFORMS, vol. 69(7), pages 3778-3796, July.
    3. Ozan Candogan & Kimon Drakopoulos, 2020. "Optimal Signaling of Content Accuracy: Engagement vs. Misinformation," Operations Research, INFORMS, vol. 68(2), pages 497-515, March.
    4. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," NBER Working Papers 23089, National Bureau of Economic Research, Inc.
    5. repec:nas:journl:v:115:y:2018:p:9216-9221 is not listed on IDEAS
    6. Mina Kwon & Michael J. Barone, 2020. "A World of Mistrust: Fake News, Mistrust Mind-Sets, and Product Evaluations," Journal of the Association for Consumer Research, University of Chicago Press, vol. 5(2), pages 206-219.
    7. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 211-236, Spring.
    8. Yiangos Papanastasiou, 2020. "Fake News Propagation and Detection: A Sequential Model," Management Science, INFORMS, vol. 66(5), pages 1826-1846, May.
    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. Charlson, G., 2022. "In platforms we trust: misinformation on social networks in the presence of social mistrust," Cambridge Working Papers in Economics 2204, Faculty of Economics, University of Cambridge.
    2. Denter, Philipp & Ginzburg, Boris, 2021. "Troll Farms and Voter Disinformation," MPRA Paper 109634, University Library of Munich, Germany.
    3. Mohamed Mostagir & James Siderius, 2022. "Learning in a Post-Truth World," Management Science, INFORMS, vol. 68(4), pages 2860-2868, April.
    4. Kris Hartley & Minh Khuong Vu, 2020. "Fighting fake news in the COVID-19 era: policy insights from an equilibrium model," Policy Sciences, Springer;Society of Policy Sciences, vol. 53(4), pages 735-758, December.
    5. Kumar, Ajay & Taylor, James W., 2024. "Feature importance in the age of explainable AI: Case study of detecting fake news & misinformation via a multi-modal framework," European Journal of Operational Research, Elsevier, vol. 317(2), pages 401-413.
    6. Ka Chung Ng & Ping Fan Ke & Mike K. P. So & Kar Yan Tam, 2023. "Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2101-2122, July.
    7. Tuval Danenberg & Drew Fudenberg, 2024. "Endogenous Attention and the Spread of False News," Papers 2406.11024, arXiv.org.
    8. Gonzalo Cisternas & Jorge Vásquez, 2022. "Misinformation in Social Media: The Role of Verification Incentives," Staff Reports 1028, Federal Reserve Bank of New York.
    9. Ozan Candogan & Kimon Drakopoulos, 2020. "Optimal Signaling of Content Accuracy: Engagement vs. Misinformation," Operations Research, INFORMS, vol. 68(2), pages 497-515, March.
    10. Dana Sisak & Philipp Denter, 2024. "Information Sharing with Social Image Concerns and the Spread of Fake News," Papers 2410.19557, arXiv.org, revised Oct 2024.
    11. Samuel S. Santos & Marcelo C. Griebeler, 2022. "Can fact-checkers discipline the government?," Economics Bulletin, AccessEcon, vol. 42(3), pages 1498-1509.
    12. Mohamed Mostagir & James Siderius, 2023. "Strategic Reviews," Management Science, INFORMS, vol. 69(2), pages 904-921, February.
    13. Leopoldo Fergusson & Carlos Molina, 2020. "Facebook Causes Protests," HiCN Working Papers 323, Households in Conflict Network.
    14. Dean Neu & Gregory D. Saxton & Abu S. Rahaman, 2022. "Social Accountability, Ethics, and the Occupy Wall Street Protests," Journal of Business Ethics, Springer, vol. 180(1), pages 17-31, September.
    15. Robbett, Andrea & Matthews, Peter Hans, 2018. "Partisan bias and expressive voting," Journal of Public Economics, Elsevier, vol. 157(C), pages 107-120.
    16. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    17. Fathey Mohammed & Nabil Hasan Al-Kumaim & Ahmed Ibrahim Alzahrani & Yousef Fazea, 2023. "The Impact of Social Media Shared Health Content on Protective Behavior against COVID-19," IJERPH, MDPI, vol. 20(3), pages 1-16, January.
    18. Bartosz Wilczek, 2020. "Misinformation and herd behavior in media markets: A cross-national investigation of how tabloids’ attention to misinformation drives broadsheets’ attention to misinformation in political and business," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    19. Joël Cariolle & Yasmine Elkhateeb & Mathilde Maurel, 2022. "(Mis-)information technology: Internet use and perception of democracy in Africa," Documents de travail du Centre d'Economie de la Sorbonne 22010, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    20. Barrera, Oscar & Guriev, Sergei & Henry, Emeric & Zhuravskaya, Ekaterina, 2020. "Facts, alternative facts, and fact checking in times of post-truth politics," Journal of Public Economics, Elsevier, vol. 182(C).

    More about this item

    Keywords

    communication; misinformation; network design; platforms;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cam:camjip:2202. 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: Jake Dyer (email available below). General contact details of provider: https://janeway.econ.cam.ac.uk/ .

    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.