IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v372y2025ics0277953625002795.html
   My bibliography  Save this article

Social media prompts to encourage intervening with cancer treatment misinformation

Author

Listed:
  • Lazard, Allison J.
  • Queen, Tara Licciardello
  • Pulido, Marlyn
  • Lake, Shelby
  • Nicolla, Sydney
  • Tan, Hung-Jui
  • Charlot, Marjory
  • Smitherman, Andrew B.
  • Dasgupta, Nabarun

Abstract

Misinformation about false and potentially harmful cancer treatments and cures are shared widely on social media. Strategies to encourage the cancer community to prosocially intervene, by flagging and reporting false posts, are needed to reduce cancer treatment misinformation. Automated prompts encouraging flagging of misinformation are a promising approach to increase intervening. Prompts may be more effective with social cues for others’ actions and clear platform policies. We examined whether prompts alone (referred to as standard prompts) or social cue prompts with a policy for removing posts would lead to more intervening, less sharing, and impact cognitive predictors of the Bystander Intervention Model (e.g., responsibility). We recruited U.S. adults in cancer networks for a within-persons, longitudinal experiment (Time 1–4). We randomized the viewing order of 1) standard prompts or 2) social cue prompts and policy, switching conditions at Time 3. Prompts encouraged intervening (flagging) without leading to other unintended actions. Participants more frequently flagged misinformation (prompted, 24–33 %) than disliking (unprompted, 3–12 %) or liking (unintended, 4–35 %) on the simulated feed. Initially (Time 1–2), social cue prompts (vs. standard) encouraged more willingness to intervene and perceived responsibility, p = .01-0.03; however, there were no differences after (Time 3–4), potentially due to carryover effects. Prompts (also called warnings, nudges, or labels) alerting viewers of cancer treatment misinformation is a promising approach to encourage intervening (flagging). Prompts can be enhanced with social cues (i.e., counts of others who flagged) and clear platform policies to encourage the cancer community to reduce misinformation on social media.

Suggested Citation

  • Lazard, Allison J. & Queen, Tara Licciardello & Pulido, Marlyn & Lake, Shelby & Nicolla, Sydney & Tan, Hung-Jui & Charlot, Marjory & Smitherman, Andrew B. & Dasgupta, Nabarun, 2025. "Social media prompts to encourage intervening with cancer treatment misinformation," Social Science & Medicine, Elsevier, vol. 372(C).
  • Handle: RePEc:eee:socmed:v:372:y:2025:i:c:s0277953625002795
    DOI: 10.1016/j.socscimed.2025.117950
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953625002795
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.socscimed.2025.117950?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gordon Pennycook & Ziv Epstein & Mohsen Mosleh & Antonio A. Arechar & Dean Eckles & David G. Rand, 2021. "Shifting attention to accuracy can reduce misinformation online," Nature, Nature, vol. 592(7855), pages 590-595, April.
    2. Xue, Xiang & Ma, Haiyun & Zhao, Yuxiang (Chris) & Zhu, Qinghua & Song, Shijie, 2024. "Mitigating the influence of message features on health misinformation sharing intention in social media: Experimental evidence for accuracy-nudge intervention," Social Science & Medicine, Elsevier, vol. 356(C).
    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. Chenlong Wang & Pablo Lucas, 2024. "Efficiency of Community-Based Content Moderation Mechanisms: A Discussion Focused on Birdwatch," Group Decision and Negotiation, Springer, vol. 33(3), pages 673-709, June.
    2. Nicolás Ajzenman & Bruno Ferman & Sant’Anna Pedro C., 2023. "Discrimination in the Formation of Academic Networks: A Field Experiment on #EconTwitter," Working Papers 235, Red Nacional de Investigadores en Economía (RedNIE).
    3. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2023. "Misinformation due to asymmetric information sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    4. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    5. Xuhao Shao & Ao Li & Chuansheng Chen & Elizabeth F. Loftus & Bi Zhu, 2023. "Cross-stage neural pattern similarity in the hippocampus predicts false memory derived from post-event inaccurate information," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    6. Hong, Ziyang & Liu, Qingfu & Tse, Yiuman & Wang, Zilu, 2023. "Black mouth, investor attention, and stock return," International Review of Financial Analysis, Elsevier, vol. 90(C).
    7. John M. Carey & Andrew M. Guess & Peter J. Loewen & Eric Merkley & Brendan Nyhan & Joseph B. Phillips & Jason Reifler, 2022. "The ephemeral effects of fact-checks on COVID-19 misperceptions in the United States, Great Britain and Canada," Nature Human Behaviour, Nature, vol. 6(2), pages 236-243, February.
    8. Krishna Dasaratha & Kevin He, 2022. "Learning from Viral Content," Papers 2210.01267, arXiv.org, revised Aug 2023.
    9. W. Ahmed & D. Önkal & R. Das & S. Krishnan & F. Olan & M. Mariann Hardey & A. Alex Fenton, 2023. "Developing Techniques to Support Technological Solutions to Disinformation by Analysing Four Conspiracy Networks During COVID-19," Post-Print hal-04693779, HAL.
    10. Melchior, Cristiane & Warin, Thierry & Oliveira, Mirian, 2025. "An investigation of the COVID-19-related fake news sharing on Facebook using a mixed methods approach," Technological Forecasting and Social Change, Elsevier, vol. 213(C).
    11. Tuval Danenberg & Drew Fudenberg, 2024. "Endogenous Attention and the Spread of False News," Papers 2406.11024, arXiv.org.
    12. Jing, Fei & Zhang, Zhong & Wu, Jian-Liang & Hu, Die & Zhang, Zi-Ke, 2025. "Quantifying and predicting evolutionary networks," Chaos, Solitons & Fractals, Elsevier, vol. 191(C).
    13. Folco Panizza & Piero Ronzani & Tiffany Morisseau & Simone Mattavelli & Carlo Martini, 2023. "How do online users respond to crowdsourced fact-checking?," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    14. Lara Marie Berger & Anna Kerkhof & Felix Mindl & Johannes Münster, 2023. "Debunking “Fake News” on Social Media: Short-Term and Longer-Term Effects of Fact Checking and Media Literacy Interventions," CESifo Working Paper Series 10576, CESifo.
    15. Gonzalo Cisternas & Jorge Vásquez, 2022. "Misinformation in Social Media: The Role of Verification Incentives," Staff Reports 1028, Federal Reserve Bank of New York.
    16. Tiziana Assenza & Alberto Cardaci & Stefanie Huber, 2024. "Fake News: Susceptibility, Awareness, and Solutions," ECONtribute Policy Brief Series 065, University of Bonn and University of Cologne, Germany.
    17. Mohamed Mostagir & James Siderius, 2022. "Learning in a Post-Truth World," Management Science, INFORMS, vol. 68(4), pages 2860-2868, April.
    18. Lau, Andy, 2023. "A Model of Online Misinformation with Endogenous Reputation," Warwick-Monash Economics Student Papers 59, Warwick Monash Economics Student Papers.
    19. van Mulukom, Valerie & Pummerer, Lotte J. & Alper, Sinan & Bai, Hui & Čavojová, Vladimíra & Farias, Jessica & Kay, Cameron S. & Lazarevic, Ljiljana B. & Lobato, Emilio J.C. & Marinthe, Gaëlle & Pavela, 2022. "Antecedents and consequences of COVID-19 conspiracy beliefs: A systematic review," Social Science & Medicine, Elsevier, vol. 301(C).
    20. Valentin Guigon & Marie Claire Villeval & Jean-Claude Dreher, 2024. "Metacognition biases information seeking in assessing ambiguous news," Post-Print hal-04848999, HAL.

    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:eee:socmed:v:372:y:2025:i:c:s0277953625002795. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    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.