Dolor y desinformación en redes sociales: representaciones digitales, amplificación y validación social en la comunicación en salud (2021–2025)

Enrique Barros-Beracaza; Stella Martínez- Rodrigo; Francisco García García

doi:10.65598/rps.6178

Authors

Enrique Barros-Beracaza Medico y cirujano, Geriatra, Máster en dolor, Doctorando en Comunicación Audiovisual y Periodismo, Universidad de Granada, Granada, España
Stella Martínez- Rodrigo Profesora titular Comunicación Audiovisual y Publicidad https://orcid.org/0000-0002-8063-0525
Francisco García García Catedrático de Comunicación Audiovisual y Publicidad Departamento TAC Facultad de Ciencias de la Información (UCM) https://orcid.org/0000-0001-5394-4804

DOI:

https://doi.org/10.65598/rps.6178

Keywords:

pain communication, health misinformation, social networks, social validation, narrative representation

Abstract

Background: Pain-related health misinformation circulates on social media, yet little is known about how narrative representations, platform sociotechnical environments (as proxies for visibility conditions), and community signals relate to its presence and spread. Comparative evidence integrating representation, credibility cues, social validation, and engagement remains limited. This study analyzes how experiences of pain are digitally represented on social media and how these representations are associated with the presence of misinformation, content amplification, credibility markers, and social validation of testimony.

Methods: We analyzed 550 public pain-related posts (110 per platform; ≥50 comments) from Facebook, Instagram, TikTok, X, and YouTube (2021–2025). We coded narrative representation, credibility markers, social validation, and engagement. Adjusted odds ratios were estimated using multivariable logistic regression with robust standard errors, alongside predicted probabilities and ROC curves. A qualitative subsample (N=300) supported complementary thematic analysis.

Results: Overall misinformation prevalence was 17.27% (95/550). In the main model, the Spectacle representation was associated with higher odds of misinformation (aOR 11.03; 95% CI 3.50–34.80), while Testimonial posts showed lower odds (aOR 0.17; 95% CI 0.04–0.75). Instagram had the highest adjusted probability (Pr=0.3945) and YouTube the lowest (Pr=0.0238). Amplification was positively associated with misinformation (engagement_log_z: aOR 1.79; 95% CI 1.28–2.52), and median raw engagement was 35,159 versus 4,993 in non-misinformation posts. In extended analyses, empathic social validation showed a protective gradient (V5 vs V1: aOR 0.0500; 95% CI 0.0165–0.1515), and declared professional identity was associated with lower odds (aOR 0.36; 95% CI 0.19–0.70). Model discrimination was AUC=0.884 (95% CI 0.806–0.905).

Conclusions: Pain-related misinformation was associated with specific narrative frames, marked platform heterogeneity, and greater amplification. Declared professional identity and empathic social validation were inversely associated with misinformation in extended models. Findings support platform-specific mitigation, digital health literacy, and message-design strategies emphasizing framing, retrievable traceability, and clinical safety.

Downloads

Download data is not yet available.

Author Biographies

Stella Martínez- Rodrigo, Profesora titular Comunicación Audiovisual y Publicidad

Professor of Audiovisual Communication and Advertising, University of Granada.

Francisco García García, Catedrático de Comunicación Audiovisual y Publicidad Departamento TAC Facultad de Ciencias de la Información (UCM)

Professor of Audiovisual Communication and Advertising Department of TAC, Faculty of Information Sciences (UCM) Director of the International Doctoral School of the University of Design, Innovation and Technology (UDIT).

References

Adler, H., Lewis, M., Ng, C. H. M., Brooks, C., Leonardi, M., Mikocka-Walus, A., Bush, D., Semprini, A., Wilkinson-Tomey, J., Condous, G., Patravali, N., Abbott, J., & Armour, M. (2024). Social media, endometriosis, and evidence-based information: An analysis of Instagram content. Healthcare, 12(1), 121. https://doi.org/10.3390/healthcare12010121

Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211–236. https://doi.org/10.1257/jep.31.2.211

Basch, C. H., Donelle, L., Fera, J., & Jaime, C. (2022). Deconstructing TikTok videos on mental health: Cross-sectional, descriptive content analysis. JMIR Formative Research, 6(5), e38340. https://doi.org/10.2196/38340

Basch, C. H., Meleo-Erwin, Z., Fera, J., Jaime, C., & Basch, C. E. (2021). A global pandemic in the time of viral memes: COVID-19 vaccine misinformation on TikTok. Human Vaccines & Immunotherapeutics, 17(8), 2373–2377. https://doi.org/10.1080/21645515.2021.1894896

Blease, C., Carel, H., & Geraghty, K. (2017). Epistemic injustice in healthcare encounters: Evidence from chronic fatigue syndrome. Journal of Medical Ethics, 43(8), 549–557. https://doi.org/10.1136/medethics-2016-103691

Brennen, J. S., Simon, F. M., Howard, P. N., & Nielsen, R. K. (2020, April 7). Types, sources, and claims of COVID-19 misinformation (RISJ Factsheet). Reuters Institute for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk/types-sources-and-claims-covid-19-misinformation

Calleja, N., AbdAllah, A., Abad, N., Ahmed, N., Albarracín, D., Altieri, E., Anoko, J. N., Arcos, R., Azlan, A. A., Bayer, J., Bechmann, A., Bezbaruah, S., Briand, S. C., Brooks, I., Bucci, L. M., Burzo, S., Czerniak, C., De Domenico, M., Dunn, A. G., ... Purnat, T. D. (2021). A public health research agenda for managing infodemics: Methods and results of the first WHO infodemiology conference. JMIR Infodemiology, 1(1), e30979. https://doi.org/10.2196/30979

Charon, R. (2006). Narrative medicine: Honoring the stories of illness. Oxford University Press.

Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2020). The COVID-19 social media infodemic. Scientific Reports, 10(1), 16598. https://doi.org/10.1038/s41598-020-73510-5

D’Agostino, E. M., Ehrhart, M. G., & Widrick, R. (2022). Content analysis of TikTok videos about chronic musculoskeletal pain. Pain Medicine, 23(10), 1672–1680. https://doi.org/10.1093/pm/pnac011

Eysenbach, G. (2020). How to fight an infodemic: The four pillars of infodemic management. Journal of Medical Internet Research, 22(6), e21820. https://doi.org/10.2196/21820

Frank, A. W. (1995). The wounded storyteller: Body, illness, and ethics. University of Chicago Press.

Franzke, A. S., Bechmann, A., Ess, C. M., & Zimmer, M. (Eds.). (2020). Internet research: Ethical guidelines 3.0. AoIR (The International Association of Internet Researchers). https://aoir.org/reports/ethics3.pdf

Gallotti, R., Valle, F., Castaldo, N., Sacco, P., & De Domenico, M. (2020). Assessing the risks of “infodemics” in response to COVID-19 epidemics. Nature Human Behaviour, 4(12), 1285–1293. https://doi.org/10.1038/s41562-020-00994-6

Gillespie, T. (2018). Custodians of the Internet: Platforms, content moderation, and the hidden decisions that shape social media. Yale University Press.

Goudman, L., De Smedt, A., Noppen, M., Moens, M., Demeyer, H., & Lenoir, D. (2022). “I am in pain”: Analysis of biomedical pain-related content and quality of information on Instagram. Journal of Pain Research, 15, 1913–1925. https://doi.org/10.2147/JPR.S359009

Hwang, D. Y., Wang, Y., Smith, I. A., Danler, J., Su, R., Ward, M., ... Kress, J. P. (2025). Physician–patient communication and discordance in chronic pain decision-making: A systematic review. Pain Medicine, 26(6), 649–661. https://doi.org/10.1093/pm/pnaf089

International Association for the Study of Pain. (2020, July 14). IASP announces revised definition of pain. https://www.iasp-pain.org/resources/iasp-news/iasp-announces-revised-definition-of-pain/

Islam, M. S., Sarkar, T., Khan, S. H., Mostofa Kamal, A.-H., Hasan, S. M. M., Kabir, A., ... Seale, H. (2020). COVID-19-related infodemic and its impact on public health: A global social media analysis. Social Network Analysis and Mining, 10, 82.

Javadi, A. H., Costa, A., & Jannini, T. B. (2023). Digital health misinformation: A systematic review of social media content and interventions. International Journal of Medical Informatics, 172, 105024.

Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S. A., Sunstein, C. R., Thorson, E. A., Watts, D. J., & Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 1094–1096. https://doi.org/10.1126/science.aao2998

Lewandowsky, S., Cook, J., Schmid, P., Holford, D. L., Finn, A., Leask, J., Thomson, A., Lombardi, D., Al-Rawi, A. K., Amazeen, M. A., Anderson, E. C., Armaos, K. D., Betsch, C., Bruns, H. H. B., Ecker, U. K. H., Gavaruzzi, T., Hahn, U., Herzog, S., Juanchich, M., ... Vraga, E. K. (2021). The COVID-19 vaccine communication handbook: A practical guide for improving vaccine communication and fighting misinformation. https://sks.to/c19vax

Lin, Y., Li, Y., & Zhang, Z. (2016). Understanding credibility and trust in online health information: A meta-analysis. Health Communication, 31(3), 341–356.

Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2021). Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature Human Behaviour, 5(3), 337–348. https://doi.org/10.1038/s41562-021-01056-1

Melzack, R., & Wall, P. D. (1965). Pain mechanisms: A new theory. Science, 150(3699), 971–979. https://doi.org/10.1126/science.150.3699.971

Merolli, M., Gray, K., & Martin-Sanchez, F. (2015). Health outcomes and related effects of using social media in chronic disease management: A literature review and analysis of affordances. Journal of Biomedical Informatics, 46(6), 957–969. https://doi.org/10.1016/j.jbi.2013.04.010

Metzger, M. J., & Flanagin, A. J. (2013). Credibility and trust of information in online environments: The use of cognitive heuristics. Journal of Pragmatics, 59, 210–220. https://doi.org/10.1016/j.pragma.2013.07.012

Nunes, J., Silva, A., Rodrigues, F., & Lima, R. (2021). Pain on TikTok: A content analysis of chronic pain narratives. Pain Management Nursing, 22(4), 1–12.

O’Connor, C., & Weatherall, J. O. (2019). The misinformation age: How false beliefs spread. Yale University Press.

Papadamou, K., Zannettou, S., Blackburn, J., De Cristofaro, E., Stringhini, G., & Sirivianos, M. (2022). It is just a flu: Assessing the magnitude of COVID-19 misinformation on YouTube. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 254–265. https://doi.org/10.1609/icwsm.v16i1.19329

Raja, S. N., Carr, D. B., Cohen, M., Finnerup, N. B., Flor, H., Gibson, S., ... Vader, K. (2020). The revised International Association for the Study of Pain definition of pain: Concepts, challenges, and compromises. Pain, 161(9), 1976–1982. https://doi.org/10.1097/j.pain.0000000000001939

Rogers, C. (1961). On becoming a person: A therapist’s view of psychotherapy. Houghton Mifflin.

Roter, D., & Hall, J. (2006). Doctors talking with patients/patients talking with doctors: Improving communication in medical visits. Praeger.

Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22–36. https://doi.org/10.1145/3137597.3137600

Suárez-Lledó, V., & Álvarez-Gálvez, J. (2021). Prevalence of health misinformation on social media: Systematic review. Journal of Medical Internet Research, 23(1), e17187. https://doi.org/10.2196/17187

Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In M. J. Metzger & A. J. Flanagin (Eds.), Digital media, youth, and credibility (pp. 73–100). The MIT Press.

Tangcharoensathien, V., Calleja, N., Nguyen, T., Purnat, T., D’Agostino, M., Garcia-Saiso, S., ... Briand, S. (2020). Framework for managing the COVID-19 infodemic: Methods and results of an online, crowdsourced WHO technical consultation. Journal of Medical Internet Research, 22(6), e19659. https://doi.org/10.2196/19659

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Turk, D. C., & Okifuji, A. (2002). Psychological factors in chronic pain: Evolution and revolution. Journal of Consulting and Clinical Psychology, 70(3), 678–690. https://doi.org/10.1037/0022-006X.70.3.678

Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559

Vraga, E. K., & Bode, L. (2020). Defining misinformation and understanding its bounded nature. Political Communication, 37(1), 136–144. https://doi.org/10.1080/10584609.2019.1678892

Vraga, E. K., Tully, M., Kotcher, J. E., Smithson, A.-B., & Broeckelman-Post, M. (2020). Theorizing news literacy behaviors. Communication Theory, 0, 1–21. https://doi.org/10.1093/ct/qtaa005

Wardle, C., & Derakhshan, H. (2017, September 27). Information disorder: Toward an interdisciplinary framework for research and policy making (Council of Europe report No. DGI(2017)09; Ref. 162317GBR). Council of Europe. https://edoc.coe.int/en/media/7495-information-disorder-toward-an-interdisciplinary-framework-for-research-and-policy-making.html

Waszak, P. M., Kasprzycka-Waszak, W., & Kubanek, A. (2018). The spread of medical fake news in social media-The pilot quantitative study. Health Policy and Technology, 7(2), 115–118. https://doi.org/10.1016/j.hlpt.2018.03.002

World Health Organization. (2020, September 23). Managing the COVID-19 infodemic: Promoting healthy behaviours and mitigating the harm from misinformation and disinformation. https://www.who.int/news/item/23-09-2020-managing-the-covid-19-infodemic-promoting-healthy-behaviours-and-mitigating-the-harm-from-misinformation-and-disinformation

World Health Organization. (2021). An overview of infodemic management during COVID-19, January 2020–May 2021. https://www.who.int/publications/i/item/9789240035966

World Medical Association. (2024). WMA Declaration of Helsinki-Ethical principles for medical research involving human participants. https://www.wma.net/policies-post/wma-declaration-of-helsinki/

Yalamanchili, B., & Basch, C. H. (2023). Quality of pain-related information on YouTube: A content analysis. Journal of Medical Internet Research, 25, e43210. https://doi.org/10.2196/43210

Yeung, A. W. K., Tosevska, A., Klager, E., Eibensteiner, F., Tsagkaris, C., Parvanov, E. D., Nawaz, F. A., Völkl-Kernstock, S., Schaden, E., Kletecka-Pulker, M., Willschke, H., & Atanasov, A. G. (2022). Medical and health-related misinformation on social media: Bibliometric study of the scientific literature. Journal of Medical Internet Research, 24(1), e28152. https://doi.org/10.2196/28152

Zheluk, A., Anderson, J., & Dineen-Griffin, S. (2022). Analysis of acute non-specific back pain content on TikTok: An exploratory study. Cureus, 14(1), e21404. https://doi.org/10.7759/cureus.21404