Skip to main content
SpringerLink
Log in

Stopping fake news: Who should be banned?

  • Regular Paper
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

Fake news and misinformation spread in online social networks in a manner similar to contagious diseases. One possibility to thwart the contagion cascade is to selectively remove a small number of nodes from the network. Although most of the literature has focused on the selection of those nodes on the basis of their topological position in the network, we pose that attributes of the nodes themselves can be more relevant in certain situations. In order to demonstrate this hypothesis, we introduce a new model of news propagation that accounts for nodes’ attributes. In particular, we introduce three important characteristics of a node: the influence capacity, the resistance to be influenced and the resistance to become an information spreader. Besides offering an intuitive justification for the model and these new parameters, we relate them to other proposals in the literature. Under the new model and using numerical simulations on both synthetic and real life networks, we show that nodes’ attributes can be more important than their graph structural properties in choosing an adequate set of vertices to be removed with the purpose of mitigating fake news propagation. Furthermore, our results suggest that removal of nodes with high influence power is more effective in denser networks and when the influence of a few nodes is much larger than that of the general population.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Flat Earth Society: The Flat Earth Society website. https://www.theflatearthsociety.org/. Accessed Feb 2024 (2024)

  2. Von Däniken, E.: Chariots of the Gods. Penguin, New York (1999)

    Google Scholar 

  3. BBC News: Flat Earth: How did YouTube help spread a conspiracy theory? https://www.bbc.com/reel/video/p07h3yc0/flat-earth-how-did-youtube-help-spread-a-conspiracy-theory-. Accessed Feb 2024 (2019)

  4. Melo, L.W.S., Passos, M.M., Salvi, R.F.: Analysis of ‘flat-earther’ posts on social media: Reflections for science education from the discursive perspective of foucault. Rev. Bras. Pesquisa Educ Ciências 20, 295–313 (2020)

    Google Scholar 

  5. Galhardi, C.P., Freire, N.P., Minayo, M.C.S., Fagundes, M.C.M.: Fato ou fake? Uma análise da desinformação frente à pandemia da covid-19 no Brasil. Ciência & Saúde Coletiva 25, 4201–4210 (2020)

    Article  Google Scholar 

  6. Jamieson, K.H.: How to debunk misinformation about covid, vaccines and masks. Sci. Am. 324(4), 44–51 (2021)

    Google Scholar 

  7. BBC News: The vaccine misinformation battle raging in France. https://www.bbc.com/news/blogs-trending-56526265. Accessed Feb 2024 (2021)

  8. Hotez, P., Batista, C., Ergonul, O., Figueroa, J.P., Gilbert, S., Gursel, M., Hassanain, M., Kang, G., Kim, J.H., Lall, B., et al.: Correcting Covid-19 vaccine misinformation: Lancet commission on Covid-19 vaccines and therapeutics task force members. EClinicalMedicine 33, 100780 (2021)

    Article  Google Scholar 

  9. World Health Organization: How to report misinformation online. https://www.who.int/campaigns/connecting-the-world-to-combat-coronavirus/how-to-report-misinformation-online. Accessed February 2024 (2024)

  10. Cardoso, T.: Desinformação sobre vacina da COVID-19 aumenta com o início da imunização. https://jornal.usp.br/universidade/desinformacao-sobre-vacina-da-covid-19-aumenta-com-o-inicio-da-imunizacao/. Accessed Feb 2024 (2021)

  11. Facebook for the media: Working to Stop Misinformation and False News. https://www.facebook.com/formedia/blog/working-to-stop-misinformation-and-false-news. Accessed Feb 2024 (2017)

  12. Iosifidis, P., Nicoli, N.: The battle to end fake news: a qualitative content analysis of Facebook announcements on how it combats disinformation. Int. Commun. Gaz. 82(1), 60–81 (2020)

    Article  Google Scholar 

  13. Mantzarlis, A.: COVID-19: \$6.5 million to help fight coronavirus misinformation. https://blog.google/outreach-initiatives/google-news-initiative/covid-19-65-million-help-fight-coronavirus-misinformation/. Accessed Feb 2024 (2020)

  14. Figueira, Á., Oliveira, L.: The current state of fake news: challenges and opportunities. Procedia Comput. Sci. 121, 817–825 (2017)

    Article  Google Scholar 

  15. Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., Krause, A.: Fake news detection in social networks via crowd signals. In: Companion Proceedings of the The Web Conference 2018, pp. 517–524 (2018)

  16. David, L., Baum, M., Benkler, Y., Berinsky, A., Greenhill, K., Menczer, F., Metzger, M., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S.A., Sunstein, C.R., Thorson, E.A., Watts, D.J., Zittrain, J.L.: The science of fake news: addressing fake news requires a multidisciplinary effort. Science 359(8), 1094–1096 (2018)

    Google Scholar 

  17. Zhou, X., Zafarani, R., Shu, K., Liu, H.: Fake news: Fundamental theories, detection strategies and challenges. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 836–837 (2019)

  18. Twitter Inc.: Permanent suspension of @realDonaldTrump. https://blog.twitter.com/en_us/topics/company/2020/suspension.html. Accessed Feb 2024 (2021)

  19. Dwoskin, E., Tiku, N.: How Twitter, on the front lines of history, finally decided to ban Trump. https://www.washingtonpost.com/technology/2021/01/16/how-twitter-banned-trump/. Accessed Feb 2024 (2021)

  20. Applebaum, A.: Trump Is a Super-Spreader of Disinformation. https://www.theatlantic.com/ideas/archive/2020/10/trump-super-spreader-disinformation/616604/. Accessed Feb 2024 (2020)

  21. Paul, C., Matthews, M.: The Russian “Firehose of Falsehood” Propaganda Model: Why It Might Work and Options to Counter It. RAND Corporation, Santa Monica, CA (2016)

  22. Becket, L.: Misinformation ‘superspreaders’: COVID vaccine falsehoods still thriving on Facebook and Instagram. https://www.theguardian.com/world/2021/jan/06/facebook-instagram-urged-fight-deluge-anti-covid-vaccine-falsehoods. Accessed Feb 2024 (2021)

  23. Sinclair, H.C.: 7 ways to avoid becoming a misinformation superspreader. https://ideas.ted.com/7-ways-to-avoid-becoming-a-misinformation-superspreader/. Accessed Feb 2024 (2021)

  24. Watts, D.J., Dodds, P.S.: Influentials, networks, and public opinion formation. J. Consum. Res. 34(4), 441–458 (2007)

    Article  Google Scholar 

  25. Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 65–74 (2011)

  26. Hearn, A., Schoenhoff, S.: From celebrity to influencer. In: Marshall, P.D., Redmond, S. (eds.) A Companion to Celebrity, pp. 194–212. Wiley, Malden (2016) . (Chap. 11)

    Google Scholar 

  27. Woods, S.: # sponsored: The emergence of influencer marketing. Technical report, TRACE: Tennessee Research and Creative Exchange, University of Tennessee (2016)

  28. De Veirman, M., Cauberghe, V., Hudders, L.: Marketing through instagram influencers: the impact of number of followers and product divergence on brand attitude. Int. J. Advert. 36(5), 798–828 (2017)

    Article  Google Scholar 

  29. Jin, S.V., Muqaddam, A., Ryu, E.: Instafamous and social media influencer marketing. Mark. Intell. Plan. 37, 567–579 (2019)

    Article  Google Scholar 

  30. Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)

    Article  Google Scholar 

  31. Pei, S., Makse, H.A.: Spreading dynamics in complex networks. J. Stat. Mech: Theory Exp. 2013(12), 12002 (2013)

    Article  Google Scholar 

  32. Pei, S., Muchnik, L., Andrade, J.S., Jr., Zheng, Z., Makse, H.A.: Searching for superspreaders of information in real-world social media. Sci. Rep. 4(1), 1–12 (2014)

    Article  Google Scholar 

  33. Taha, K.: Identifying the top-k influential spreaders in social networks: a survey and experimental evaluation. IEEE Access 10, 107809–107845 (2022)

    Article  Google Scholar 

  34. Andrade, R.L., Rêgo, L.C., Silva, T.L.C., Macêdo, J.A.F., Silva, W.C.: Energy disruptive centrality with an application to criminal network. Commun. Nonlinear Sci. Numer. Simul. 99, 105834 (2021)

    Article  MathSciNet  Google Scholar 

  35. Hegselmann, R., Krause, U., et al.: Opinion dynamics and bounded confidence models, analysis, and simulation. J. Artif. Soc. Soc. Simul. 5(3), 1–33 (2002)

    Google Scholar 

  36. Kuperman, M., Zanette, D.: Stochastic resonance in a model of opinion formation on small-world networks. Eur. Phys. J. B-Condens. Matter Complex Syst. 26(3), 387–391 (2002)

    Article  Google Scholar 

  37. Tessone, C.J., Toral, R.: System size stochastic resonance in a model for opinion formation. Physica A 351(1), 106–116 (2005)

    Article  Google Scholar 

  38. Holme, P., Newman, M.E.: Nonequilibrium phase transition in the coevolution of networks and opinions. Phys. Rev. E 74(5), 056108 (2006)

    Article  Google Scholar 

  39. Li, P.-P., Zheng, D.-F., Hui, P.: Dynamics of opinion formation in a small-world network. Phys. Rev. E 73(5), 056128 (2006)

    Article  Google Scholar 

  40. Nardini, C., Kozma, B., Barrat, A.: Who’s talking first? Consensus or lack thereof in coevolving opinion formation models. Phys. Rev. Lett. 100(15), 158701 (2008)

    Article  Google Scholar 

  41. Tessone, C.J., Toral, R.: Diversity-induced resonance in a model for opinion formation. Eur. Phys. J. B 71(4), 549–555 (2009)

    Article  Google Scholar 

  42. Jalili, M.: Social power and opinion formation in complex networks. Physica A 392(4), 959–966 (2013)

    Article  MathSciNet  Google Scholar 

  43. Acemoğlu, D., Como, G., Fagnani, F., Ozdaglar, A.: Opinion fluctuations and disagreement in social networks. Math. Oper. Res. 38(1), 1–27 (2013)

    Article  MathSciNet  Google Scholar 

  44. Gionis, A., Terzi, E., Tsaparas, P.: Opinion maximization in social networks. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 387–395 (2013). SIAM

  45. Sîrbu, A., Loreto, V., Servedio, V.D., Tria, F.: Opinion dynamics: models, extensions and external effects. In: Loreto, V., Haklay, M., Hotho, A., Servedio, V.D.P., Stumme, G., Theunis, J., Tria, F. (eds.) Participatory Sensing, Opinions and Collective Awareness, pp. 363–401. Springer, Cham (2017)

    Chapter  Google Scholar 

  46. Medo, M., Mariani, M.S., Lü, L.: The fragility of opinion formation in a complex world. Commun. Phys. 4(1), 1–10 (2021)

    Article  Google Scholar 

  47. Hethcote, H.W.: The mathematics of infectious diseases. SIAM Rev. 42(4), 599–653 (2000)

    Article  MathSciNet  Google Scholar 

  48. Pastor-Satorras, R., Castellano, C., Van Mieghem, P., Vespignani, A.: Epidemic processes in complex networks. Rev. Mod. Phys. 87(3), 925 (2015)

    Article  MathSciNet  Google Scholar 

  49. Katz, E.: The two-step flow of communication: An up-to-date report on an hypothesis. Public Opin. Q. 21(1), 61–78 (1957)

    Article  Google Scholar 

  50. Nisbet, M.C., Kotcher, J.E.: A two-step flow of influence? Opinion-leader campaigns on climate change. Sci. Commun. 30(3), 328–354 (2009)

    Article  Google Scholar 

  51. Weimann, G.: The influentials: back to the concept of opinion leaders? Public Opin. Q. 55(2), 267–279 (1991)

    Article  Google Scholar 

  52. Trepte, S., Scherer, H.: Opinion leaders-do they know more than others about their area of interest? Communications 35(2), 119–140 (2010)

    Article  Google Scholar 

  53. Page, B.I., Shapiro, R.: Presidents as opinion leaders: Some new evidence. Policy Stud. J. 12(4), 649 (1984)

    Article  Google Scholar 

  54. Venkatraman, M.P.: Opinion leaders, adopters, and communicative adopters: a role analysis. Psychol. Mark. 6(1), 51–68 (1989)

    Article  Google Scholar 

  55. Park, C.S.: Does twitter motivate involvement in politics? tweeting, opinion leadership, and political engagement. Comput. Hum. Behav. 29(4), 1641–1648 (2013)

    Article  Google Scholar 

  56. Winter, S., Neubaum, G.: Examining characteristics of opinion leaders in social media: a motivational approach. Soc. Med. Soc. 2(3), 2056305116665858 (2016)

    Google Scholar 

  57. Valente, T.W., Pumpuang, P.: Identifying opinion leaders to promote behavior change. Health Educ. Behav. 34(6), 881–896 (2007)

    Article  Google Scholar 

  58. Bamakan, S.M.H., Nurgaliev, I., Qu, Q.: Opinion leader detection: a methodological review. Expert Syst. Appl. 115, 200–222 (2019)

    Article  Google Scholar 

  59. Nie, Y., Li, W., Pan, L., Lin, T., Wang, W.: Markovian approach to tackle competing pathogens in simplicial complex. Appl. Math. Comput. 417, 126773 (2022)

    MathSciNet  Google Scholar 

  60. Nie, Y., Zhong, X., Lin, T., Wang, W.: Homophily in competing behavior spreading among the heterogeneous population with higher-order interactions. Appl. Math. Comput. 432, 127380 (2022)

    MathSciNet  Google Scholar 

  61. Newman, M.: Networks. Oxford University Press, Oxford (2018)

    Book  Google Scholar 

  62. Bonacich, P.: Some unique properties of eigenvector centrality. Soc. Netw. 29(4), 555–564 (2007)

    Article  Google Scholar 

  63. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)

    Article  Google Scholar 

  64. Anthonisse, J.M.: The rush in a directed graph. Technical Report BN 9/71, Stichting Mathematisch Centrum. Mathematische Besliskunde (1971)

  65. Freeman, L.C.: Centrality in social networks. Conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)

    Google Scholar 

  66. Xiong, F., Liu, Y., Zhang, Z.-J., Zhu, J., Zhang, Y.: An information diffusion model based on retweeting mechanism for online social media. Phys. Lett. A 376(30–31), 2103–2108 (2012)

    Article  Google Scholar 

  67. Melnik, S., Ward, J.A., Gleeson, J.P., Porter, M.A.: Multi-stage complex contagions. Chaos 23(1), 013124 (2013)

    Article  MathSciNet  Google Scholar 

  68. Huang, W.-M., Zhang, L.-J., Xu, X.-J., Fu, X.: Contagion on complex networks with persuasion. Sci. Rep. 6(1), 1–8 (2016)

    Google Scholar 

  69. Centola, D., Macy, M.: Complex contagions and the weakness of long ties. Am. J. Sociol. 113(3), 702–734 (2007)

    Article  Google Scholar 

  70. Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, pp. 519–528 (2012)

  71. Gleeson, J.P.: Cascades on correlated and modular random networks. Phys. Rev. E 77(4), 046117 (2008)

    Article  Google Scholar 

  72. Dodds, P.S., Payne, J.L.: Analysis of a threshold model of social contagion on degree-correlated networks. Phys. Rev. E 79(6), 066115 (2009)

    Article  Google Scholar 

  73. Gai, P., Kapadia, S.: Contagion in financial networks. Proc. R. Soc. A: Math. Phys. Eng. Sci. 466(2120), 2401–2423 (2010)

    Article  MathSciNet  Google Scholar 

  74. Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 695–704 (2011)

  75. Barash, V., Cameron, C., Macy, M.: Critical phenomena in complex contagions. Soc. Netw. 34(4), 451–461 (2012)

    Article  Google Scholar 

  76. Dodds, P.S., Harris, K.D., Danforth, C.M.: Limited imitation contagion on random networks: chaos, universality, and unpredictability. Phys. Rev. Lett. 110(15), 158701 (2013)

    Article  Google Scholar 

  77. Karimi, F., Holme, P.: Threshold model of cascades in empirical temporal networks. Physica A 392(16), 3476–3483 (2013)

    Article  Google Scholar 

  78. Wang, W., Tang, M., Zhang, H.-F., Lai, Y.-C.: Dynamics of social contagions with memory of nonredundant information. Phys. Rev. E 92(1), 012820 (2015)

    Article  Google Scholar 

  79. González-Bailón, S., Borge-Holthoefer, J., Rivero, A., Moreno, Y.: The dynamics of protest recruitment through an online network. Sci. Rep. 1(1), 1–7 (2011)

    Article  Google Scholar 

  80. Centola, D.: An experimental study of homophily in the adoption of health behavior. Science 334(6060), 1269–1272 (2011)

    Article  Google Scholar 

  81. Centola, D.M.: Homophily, networks, and critical mass: solving the start-up problem in large group collective action. Ration. Soc. 25(1), 3–40 (2013)

    Article  MathSciNet  Google Scholar 

  82. Kim, D.A., Hwong, A.R., Stafford, D., Hughes, D.A., O’Malley, A.J., Fowler, J.H., Christakis, N.A.: Social network targeting to maximise population behaviour change: a cluster randomised controlled trial. Lancet 386(9989), 145–153 (2015)

    Article  Google Scholar 

  83. DellaPosta, D., Nee, V., Opper, S.: Endogenous dynamics of institutional change. Ration. Soc. 29(1), 5–48 (2017)

    Article  Google Scholar 

  84. Aral, S., Dhillon, P.S.: Social influence maximization under empirical influence models. Nat. Hum. Behav. 2(6), 375–382 (2018)

    Article  Google Scholar 

  85. Guilbeault, D., Centola, D.: Topological measures for identifying and predicting the spread of complex contagions. Nat. Commun. 12(1), 1–9 (2021)

    Article  Google Scholar 

  86. Aral, S., Muchnik, L., Sundararajan, A.: Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc. Natl. Acad. Sci. 106(51), 21544–21549 (2009)

    Article  Google Scholar 

  87. Centola, D.: Failure in complex social networks. J. Math. Sociol. 33(1), 64–68 (2008)

    Article  Google Scholar 

  88. Kuhlman, C.J., Anil Kumar, V., Marathe, M.V., Ravi, S., Rosenkrantz, D.J.: Inhibiting diffusion of complex contagions in social networks: theoretical and experimental results. Data Min. Knowl. Disc. 29(2), 423–465 (2015)

    Article  MathSciNet  Google Scholar 

  89. Carscadden, H.L., Kuhlman, C.J., Marathe, M.V., Ravi, S., Rosenkrantz, D.J.: Blocking the propagation of two simultaneous contagions over networks. In: International Conference on Complex Networks and Their Applications, pp. 455–468 (2020). Springer

  90. Lalou, M., Tahraoui, M.A., Kheddouci, H.: The critical node detection problem in networks: a survey. Comput. Sci. Rev. 28, 92–117 (2018)

    Article  MathSciNet  Google Scholar 

  91. Cavallaro, L., Costantini, S., De Meo, P., Liotta, A., Stilo, G.: Network connectivity under a probabilistic node failure model. IEEE Trans. Netw. Sci. Eng. 9(4), 2463–2480 (2022)

    Article  MathSciNet  Google Scholar 

  92. Myers, S.A., Zhu, C., Leskovec, J.: Information diffusion and external influence in networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 33–41 (2012)

  93. Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81(25), 2340–2361 (1977)

    Article  Google Scholar 

  94. Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  95. Bakshy, E., Karrer, B., Adamic, L.A.: Social influence and the diffusion of user-created content. In: Proceedings of the 10th ACM Conference on Electronic Commerce, pp. 325–334 (2009)

  96. De Domenico, M., Lima, A., Mougel, P., Musolesi, M.: The anatomy of a scientific rumor. Sci. Rep. 3(1), 1–9 (2013)

    Article  Google Scholar 

  97. Bayer, J., Bitiukova, N., Bard, P., Szakács, J., Alemanno, A., Uszkiewicz, E.: Disinformation and propaganda-impact on the functioning of the rule of law in the eu and its member states. European Parliament, LIBE Committee, Policy Department for Citizens’ Rights and Constitutional Affairs (2019)

  98. Bayer, J., Holznagel, D.B., Katarzyna, L., Adela, P., Josephine, B.S., Szakács, J., Uszkiewicz, E.: Disinformation and propaganda: impact on the functioning of the rule of law and democratic processes in the eu and its member states: 2021 update (2021)

  99. Bontcheva, K., Posetti, J. (eds.): Balancing Act: Countering Digital Disinformation While Respecting Freedom of Expression. Broadband Commission Research Report on ‘Freedom of Expression and Addressing Disinformation on the Internet’. ITU, UNESCO, Geneva and Paris (2020)

  100. Marsden, C., Meyer, T., Brown, I.: Platform values and democratic elections: how can the law regulate digital disinformation? Comput. Law Secur. Rev. 36, 105373 (2020)

    Article  Google Scholar 

  101. Kyza, E.A., Varda, C., Panos, D., Karageorgiou, M., Komendantova-Amann, N., Perfumi, S.C., Shah, S.I.H., Hosseini, A.S.: Combating misinformation online: re-imagining social media for policy-making. Internet Policy Rev. 9(4), 1–24 (2020)

    Article  Google Scholar 

  102. Ó Fathaigh, R., Helberger, N., Appelman, N.: The perils of legally defining disinformation. Amsterdam Law School Research Paper, 2022-05 (2021)

Download references

Funding

This work was funded by CNPq Grant 308980/2021-2.

Author information

Author notes

  1. Pablo Ignacio Fierens and Leandro Chaves Rêgo have contributed equally to this work.

Authors and Affiliations

  1. Instituto Tecnológico de Buenos Aires (ITBA), Iguazú 341, C1437ETC, Ciudad Autónoma de Buenos Aires, CABA, Argentina

    Pablo Ignacio Fierens

  2. Department, National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, C1425FQB, Ciudad Autónoma de Buenos Aires, CABA, Argentina

    Pablo Ignacio Fierens

  3. Departamento de Estatística e Matemática Aplicada, Universidade Federal do Ceará, Bloco 910, Campus do Pici, Fortaleza, Ceará, 60440-554, Brazil

    Leandro Chaves Rêgo

  4. Programa de Pós-Graduação em Engenharia de Produção, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, 1235, Cidade Universitária, Recife, 50.740-550, Brazil

    Leandro Chaves Rêgo

Authors
  1. Pablo Ignacio Fierens
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leandro Chaves Rêgo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.I.F. and L.C.R. equally contributed to the development of the ideas and wrote the main manuscript text. Data processing and simulations were the responsibility of P.I.F.

Corresponding author

Correspondence to Pablo Ignacio Fierens.

Ethics declarations

Conflict of interest

The authors have no conflict of interest to declare that is relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fierens, P.I., Rêgo, L.C. Stopping fake news: Who should be banned?. Int J Data Sci Anal (2024). https://doi.org/10.1007/s41060-024-00532-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41060-024-00532-x

Keywords

Search

Navigation