Abstract
This review is focused on “information bubbles” and related topics. Our objective is to identify how these bubbles can affect Health Communications and to propose ideas for scientific research. The phenomenon is complex because it involves both technological
and psychological aspects. The implications of bubbles have been discussed in the context of politics, elections, health and
intolerance in general. The “misinformation” about diseases generated in bubbles promotes real risks to life. Strategies to avoid
the impact of bubbles are also discussed.
References
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.
com/sol3/papers.cfm?abstract_id=2795110
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.
com/2018/03/10/opinion/sunday/youtube-politics-radical.html
19. Rossi E. Todos contra as Big Techs. Isto é Dinheiro [internet]. Jun 2019 [acesso em 28 mar 2020]. Disponível em https://www.istoedinheiro.com.br/todos-contra-as-big-techs/
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: https://www.semanticscholar.org/paper/It%E2%80%99s-not-the-technology%2C-
stupid%3A-How-the-%E2%80%98Echo-and-Bruns/202f6b1647c060d4d3be2e879337b27760cabfb8
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://
www.rollingstone.com/culture/culture-features/celebrities-anti-vaxxers-jessica-biel-847779/
34. Patient Like Me [internet]. Cambridge: Patients Like Me [acesso em 30 ago 2020]. Disponível em https://www.patientslikeme.com/
35. Tu Diabetes. Califórnia: Tu Diabetes [internet]. Beyond Type 1; 2020. [acesso em 30 ago 2020]. Disponível em: https://www.tudiabetes.org.
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: https://g1.globo.com/bemestar/coronavirus/
noticia/2020/03/29/videos-que-contrariam-indicacoes-de-medicos-e-cientistas-para-conter-o-coronavirus-ganham-espaco-em-grupos-politicos-no-whatsapp.ghtml
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.
com/sol3/papers.cfm?abstract_id=2795110
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.
com/2018/03/10/opinion/sunday/youtube-politics-radical.html
19. Rossi E. Todos contra as Big Techs. Isto é Dinheiro [internet]. Jun 2019 [acesso em 28 mar 2020]. Disponível em https://www.istoedinheiro.com.br/todos-contra-as-big-techs/
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: https://www.semanticscholar.org/paper/It%E2%80%99s-not-the-technology%2C-
stupid%3A-How-the-%E2%80%98Echo-and-Bruns/202f6b1647c060d4d3be2e879337b27760cabfb8
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://
www.rollingstone.com/culture/culture-features/celebrities-anti-vaxxers-jessica-biel-847779/
34. Patient Like Me [internet]. Cambridge: Patients Like Me [acesso em 30 ago 2020]. Disponível em https://www.patientslikeme.com/
35. Tu Diabetes. Califórnia: Tu Diabetes [internet]. Beyond Type 1; 2020. [acesso em 30 ago 2020]. Disponível em: https://www.tudiabetes.org.
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: https://g1.globo.com/bemestar/coronavirus/
noticia/2020/03/29/videos-que-contrariam-indicacoes-de-medicos-e-cientistas-para-conter-o-coronavirus-ganham-espaco-em-grupos-politicos-no-whatsapp.ghtml
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2020 Diego dos Santos Vega Senise, Leandro Leonardo Batista
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