Bolhas de informação e a comunicação da saúde pública


  • Diego dos Santos Vega Senise
  • Leandro Leonardo Batista Universidade de São Paulo. Escola de Comunicação e Artes



Bolhas de informação, Câmara de eco;, Viralização, Comunicação em Saúde


Este artigo propõe uma revisão de literatura sobre as chamadas bolhas de informação e temas correlatos. O objetivo é identificar
como a existências dessas bolhas pode afetar a Comunicação em Saúde e endereçar novos caminhos de pesquisa científica. O
fenômeno é complexo, pois envolve tanto aspectos tecnológicos quanto psicológicos dos cidadãos. As implicações das bolhas
têm sido discutidas no âmbito da política, eleições, saúde e de intolerância de um modo geral. A “desinformação” sobre doenças
gerada nas bolhas impulsiona riscos concretos à vida. São discutidas também estratégias para evitar o impacto das bolhas.


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01. Bode L, Vraga E K. In related news, that was wrong: The correction of misinformation through related stories functionality in social media. Journal of Communication. 2015; 65(4):619-638.
02. Keim ME, Noji E. Emergent use of social media: a new age of opportunity for disaster resilience. American journal of disaster medicine. 2011;6(1):47-54.
03. Cavazos-Rehg P, Krauss M, Grucza R, Bierut L. Characterizing the followers and tweets of a marijuana-focused Twitter handle. Journal of medical
Internet research. 2014;16(6):157.
04. Berinsky AJ. Rumors and health care reform: Experiments in political misinformation. British journal of political science. 2017;47(2):241-262.
05. Sunstein CR. Echo chambers: Bush v. Gore, impeachment, and beyond. Princeton: Princeton University Press; 2001.
06. Thurman N. Making ‘The Daily Me’: Technology, economics and habit in the mainstream assimilation of personalized news. Journalism. 2011;12(4):395-415.
07. Sunstein CR. Democracy and filtering. Communications of the ACM. 2004;47(12):57-59.
08. Quattrociocchi W, Scala A, Sunstein CR. Echo chambers on Facebook [internet]. 2016 [acesso em 30 mar 2020]. Disponível em: http://papers.ssrn.
09. Burbach L, Halbach P, Ziefle, M, Valdez, AC. Bubble Trouble: Strategies Against Filter Bubbles in Online Social Networks. In: International Conference on Human-Computer Interaction; Germany: Springer; 2019. p.441-456.
10. McNab C. What social media offers to health professionals and citizens. Bulletin of the World Health Organization. 2009;87:566-566.
11. Centola D. Social media and the science of health behavior. Circulation. 2013; 127(21):2135-2144.
12. Thackeray R, Neiger BL, Smith AK, Van Wagenen SB. Adoption and use of social media among public health departments. BMC public health. 2013;12(1):1-6.
13. Susskind J. Future politics: Living together in a world transformed by tech. Oxford: Oxford University Press;2018.
14. Pariser E. The filter bubble: How the new personalized web is changing what we read and how we think. London: Penguin; 2011.
15. Schwartz B. O paradoxo da escolha: por que mais é menos. São Paulo: A Girafa Editora; 2007.
16. Gilovich T, Griffin,D, Kahneman D, editors. Heuristics and biases: The psychology of intuitive judgment. Cambridge: Cambridge University Press; 2002.
17. Bobok D. Selective exposure, filter bubbles and echo chambers on Facebook [dissertação]. Budapeste: Central European University;2016.
18. Tufekci Z. YouTube, the great radicalizer. The New York Times. 2018 Mar 10. [acesso em 30 ago 2020]. Disponível em: https://www.nytimes.
19. Rossi E. Todos contra as Big Techs. Isto é Dinheiro [internet]. Jun 2019 [acesso em 28 mar 2020]. Disponível em
20. Bruns A. It’s not the technology, stupid: How the Echo Chamber and Filter Bubble metaphors have failed us. Sociology. [internet] 2019. [acesso em 30 ago 2020]. Disponível em:
21. Krackhardt D, Stern RN. Informal networks and organizational crises: An experimental simulation. Social Psychology Quarterly. 1988;123-140.
22. Nguyen TT, Hui PM, Harper FM, Terveen L, Konstan JA. Exploring the filter bubble: the effect of using
recommender systems on content diversity. In Proceedings of the: 23rd International Conference on World Wide Web. Apr 11 7 2014; p. 677-686, Seoul, Republic of Korea.
23. Dylko I, Dolgov I, Hoffman, W, Eckhart N, Molina M, Aaziz O. The dark side of technology: An experimental investigation of the influence of customizability technology on online political selective exposure. Computers in Human Behavior. 2017;73:181-190.
24. González RJ. Hacking the citizenry?: Personality profiling, big data and the election of Donald Trump. Anthropology Today. 2017;33(3):9-12.
25. Posner MI, Boies SJ. Components of attention. Psychological review. 1971; 78(5):391.
26. Kahneman D. Attention and effort. Englewood Cliffs: Prentice-Hall;1973.
27. Lazarsfeld P, Berelson B, Gaudet H. The People’s Choice: How the Voter Makes Up His Mind in a Presidential Campaign. The ANNALS of the
American Academy of Political and Social Science.1948;261(1):194-194.
28. Gerbner G, Gross L, Morgan M, Signorielli N. Growing up with television: The cultivation perspective. In: Bryant J, Zilmann D, editors. Media effects: advances in theory and research. New Jersey: Lawrence Erlbaum Associates, 1994. p.17-41.
29. Pimentel CE, Gunther H, Black PUV. Acessando o medo do crime: um survey por meio da internet. Psicologia Argumento. 2017;30(69).
30. Cacioppo JT, Petty RE. The elaboration likelihood model of persuasion. In: Thomas C. Kinnear, editor. NA - Advances in Consumer Research Volume 11,editors. Provo: Association for Consumer Research,1984. p. 673-675.
31. Jha A, Lin L, Savoia, E. The use of social media by state health departments in the US: analyzing health communication through Facebook. Journal of community health. 2016; 41(1):174-179.
32. Holone H. The filter bubble and its effect on online personal health information. Croatian medical journal. 2016; 57(3):298.
33. Dickson E. A Guide to 17 Anti-Vaccination Celebrities. Rolling Stones [internet]. jun 2019 [acesso em 30 ago 2020]. Disponível em: https://
34. Patient Like Me [internet]. Cambridge: Patients Like Me [acesso em 30 ago 2020]. Disponível em
35. Tu Diabetes. Califórnia: Tu Diabetes [internet]. Beyond Type 1; 2020. [acesso em 30 ago 2020]. Disponível em:
36. Kostkova P, Mano V, Larson HJ, Schulz W. S. (2016, April). Vac medi board: Analysing vaccine rumours
in news and social media. In: Proceedings of the 6th International Conference on Digital Health Conference, 2016 april. p. 163-164.
37. Ghenai A, Mejova Y. Fake cures: user-centric modeling of health misinformation in social media. In: Proceedings of the ACM on human-computer
interaction. 2018; 2:1-20.
38. Nyhan B, Reifler J, Ubel PA. The hazards of correcting myths about health care reform. Medical care. 2013;127-132.
39. Dixon GN, Clarke CE. Heightening uncertainty around certain science: Media coverage, false balance, and the autism-vaccine controversy. Science
Communication. 2013; 35(3):358-382.
40. Fowler A, Margolis M. The political consequences of uninformed voters. Electoral Studies. 2014;34:100-110.
41. Bozdag E, van den Hoven J. Breaking the filter bubble: democracy and design. Ethics and Information Technology. 2015; 17(4):249-265.
42. Resnick P, Garrett RK, Kriplean T, Munson SA, Stroud, NJ. Bursting your (filter) bubble: strategies for promoting diverse exposure. In: Proceedings of the 2013 conference on Computer supported cooperative work companion, 2013 february, p. 95-100.
43. Shu K, Sliva A, Wang S, Tang J, Liu, H. fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter. 2017;19(1):22-36.
44. Chen Y, Conroy NJ, Rubin V L. Misleading online content: recognizing clickbait as false news. In:Proceedings of the 2015 ACM on workshop on
multimodal deception detection. 2015, p. 15-19.
45. Gupta A, Lamba H, Kumaraguru P, Joshi A. Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy. In: Proceedings of the 22nd international conference on World Wide Web. 2013, p.729-736.
46. Mav J, Gao W, Wei Z, Lu Y, Wong KF. Detect rumors using time series of social context information on micro blog ging websites. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015, p.1751-175.
47. Epstein R, Robertson RE. The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections. Proceedings
of the National Academy of Sciences. 2015;112(33):4512-4521.
48. Bright J. Explaining the emergence of political fragmentation on social media: The role of ideology and extremism. Journal of Computer-Mediated
Communication. 2018;23(1):17-33.
49. DiFonzo N, Bordia P. Rumor psychology: Social and organizational approaches. Washington: American Psychological Association; 2007.
50. Lantian A, Muller D, Nurra C, Douglas KM.Measuring belief in conspiracy theories: Validation of a French and English single-item scale. International Review of Social Psychology. 2016;29(1):1-14.
51. Oppenheimer DM. The secret life of fluency. Trends in cognitive sciences. 2008; 12(6):237-241.
52. Schwarz N, Sanna LJ, Skurnik I, Yoon C. Metacognitive experiences and the intricacies of setting people straight: Implications for debiasing and public information campaigns. Advances in experimental social psychology. 2007;39: 127-161.
53. Paiva D, Lavado T. Vídeos que contrariam indicações de médicos e cientistas para conter o coronavírus ganham espaço em grupos políticos no WhatsApp [internet]. Mar [acesso em 30 ago 2020]. Disponível em:




Como Citar

dos Santos Vega Senise, D., & Batista, L. L. (2020). Bolhas de informação e a comunicação da saúde pública. BIS. Boletim Do Instituto De Saúde, 21(1), 17–30.

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