Desinformación y vacunas en redescomportamiento de los bulos en Twitter

  1. Noguera Vivo, José Manuel 1
  2. Grandío-Pérez, María del Mar 2
  3. Villar-Rodríguez, Guillermo 3
  4. Martín, Alejandro 3
  5. Camacho, David 3
  1. 1 Universidad Católica San Antonio
    info

    Universidad Católica San Antonio

    Murcia, España

    ROR https://ror.org/05b1rsv17

  2. 2 Universidad de Murcia
    info

    Universidad de Murcia

    Murcia, España

    ROR https://ror.org/03p3aeb86

  3. 3 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

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Journal:
Revista Latina de Comunicación Social

ISSN: 1138-5820

Year of publication: 2023

Issue: 81

Type: Article

DOI: 10.4185/RLCS-2022-1820 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Revista Latina de Comunicación Social

Abstract

Introduction: Anti-vaccine hoaxes are a highly dangerous type of health misinformation, given their direct effects on society. Although there is relevant research on typology of hoaxes, denialist discourses on networks or about the popularity of vaccines, this study provides a complementary and new vision, focused on the anti-vaccine discourse of COVID-19 on Twitter from the perspective of the behavior of the accounts that spread disinformation. Methodology: Using the FacTeR-Check method, with five phases and a first sample of a hundred hoaxes (December 2020 and September 2021), 220,246 tweets were downloaded, filtered to work with AI and natural language inference techniques (NLI) on a second sample of more than 36,000 tweets (N=36,292). Results: The results offer predominance of some types of disinformation production, as well as the effectiveness of creating false original content to gather followers or the identification of a period (2013-2020) of more domination of users who support hoaxes, compared to those who deny it. Discussion: The article shows how the typology of the accounts can be a predictive factor about the behavior of users who spread disinformation. Conclusions: Similar behavioral patterns of anti-vaccine discourse on Twitter are offered, which can help manage future similar phenomena. Given the significant size of the sample and the techniques used, it can be concluded that this work establishes a solid foundation for other comparative studies on disinformation and health in social networks.

Bibliographic References

  • Almansa-Martínez, A., Fernández-Torres, M. J. y Rodríguez-Fernández, L. (2022). Desinformación en España un año después de la COVID-19. Análisis de las verificaciones de Newtral y Maldita . Revista Latina de Comunicación Social, 80, 183-200. https://doi.org/10.4185/RLCS-2022-1538
  • Aparici, R., García-Marín, D. y Rincón-Manzano, L. (2019). Noticias falsas, bulos y trending to-pics. Anatomía y estrategias de la desinformación en el conflicto catalán. El Profesional de la Información, 28. https://doi.org/10.3145/epi.2019.may.13
  • Blankenship, E. B., Goff, M. E., Yin, J., Tse, Z. T. H., Fu, K. W., Liang, H. y Fung, I. C. H. (2018). Sentiment, contents, and retweets: a study of two vaccine-related Twitter datasets. The Permanente Journal, 22. https://doi.org/10.7812/tpp/17-138
  • Bodaghi, A. y Oliveira, J. (2022). The theater of fake news spreading, who plays which role? A study on real graphs of spreading on Twitter. Expert Systems with Applications, 189, https://doi.org/10.1016/j.eswa.2021.116110
  • Carrasco-Polaino, R., Martín-Cárdaba, M. y Villar-Cirujano, E. (2021). Citizen participation in Twitter: Anti-vaccine controversies in times of COVID-19. Comunicar, 69, 21-31. https://doi.org/10.3916/C69-2021-02
  • Caro-Castaño, Lucía (2015). La identidad mosaico como modo de subjetividad propio de las redes sociales digitales y sus formas de comunicación paramediáticas: La microcelebridad y la marca personal. [Tesis doctoral, Universidad de Cádiz]. https://bit.ly/3mJwShQ
  • Deiner, M. S., Fathy, C., Kim, J., Niemeyer, K., Ramirez, D., Ackley, S. F. y Porco, T. C. (2019). Facebook and Twitter vaccine sentiment in response to measles outbreaks. Health Informatics Journal, 25, 1116-1132. https://doi.org/10.1177/1460458217740723
  • Döveling, K. Harju, A. y Sommer, D. (2018). From mediatized emotion to digital affect cultures: New technologies and global flows of emotion. Social Media + Society, 4, 1-11. https://doi.org/10.1177/2056305117743141
  • El-Mohandes A., White, T.M., Wyka, K. et al. (2021). COVID-19 vaccine acceptance among adults in four major US metropolitan areas and nationwide. Scientific Reports, 11, https://doi.org/10.1038/s41598-021-00794-6
  • Evanega, S. et al. (2021). Coronavirus misinformation: quantifying sources and themes in the COVID-19 infodemic. JMIR, 10. https://bit.ly/3HoN1RM
  • Garimella, V. R. K. y Weber, I. (2017). A long-term analysis of polarization on Twitter. En Proceedings of the International AAAI Conference on Web and Social Media, Mayo (No. 1). https://bit.ly/3mCmCb5
  • Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 78(6), 1360-1380. https://bit.ly/3sD5AgO
  • Himelboim, I., Xiao, X., Lee, D. K. L., Wang, M. Y. y Borah, P. (2020). A social networks approach to understanding vaccine conversations on Twitter: Network clusters, sentiment, and certainty in HPV social networks. Health Communication, 35, 607-615. https://doi.org/10.1080/10410236.2019.1573446
  • Huertas-García, Á., Huertas-Tato, J., Martín, A. y Camacho, D. (2021). CIVIC-UPM at CheckThat! 2021: integration of transformers in misinformation detection and topic classification. Faggioli et al.[33]. https://bit.ly/3JrhwIi
  • Huertas-García, Á., Huertas-Tato, J., Martín, A. y Camacho, D. (2021). Countering Misinformation Through Semantic-Aware Multilingual Models. International Conference on Intelligent Data Engineering and Automated Learning, noviembre, 312-323. Springer. https://bit.ly/319DiiF
  • Huertas-Tato, J., Martín, A. y Camacho, D. (2021). SML: a new Semantic Embedding Alignment Transformer for efficient cross-lingual Natural Language Inference. arXiv preprint arXiv:2103.09635. https://bit.ly/3qtsbts
  • Ireton, Cherilyn, y Posetti, Julie (eds). (2018). Journalism, ‘fake news’ and disinformation: Handbook for journalism education and training. Unesco. https://bit.ly/3mGXoZd
  • Islam, M.S. et al. (2020). COVID-19-related infodemic and its impact on public health: a global social media analysis. The American Journal of Tropical Medicine and Hygiene, 103. 1621-1629. https://dx.doi.org/10.4269%2Fajtmh.20-0812
  • Kietzmann, J. H., Hermkens, K., McCarthy, I. P. y Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54, 241-251. https://bit.ly/3mGwujY
  • Kim, H. K., Ahn, J., Atkinson, L. y Kahlor, L.A. (2020). Effects of COVID-19 misinformation on information seeking, avoidance, and processing: a multi-country comparative study. Science Commun, 42. https://doi.org/10.1177/1075547020959670
  • Knuutila, A., Neudert, L. y Howard, P. (2020). Global fears of disinformation: Perceived Internet and Social Media Harms in 142 countries, Oxford Internet Institute. The project on computational propaganda, diciembre, https://bit.ly/3FEoCXD
  • Kouzy, R., Abi Jaoude, J., Kraitem, A., El Alam, M. B., Karam, B., Adib, E., Zarka, J., Traboulsi, C., Akl, E. W. y Baddour, K. (2020). Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter. Cureus, 12, https://doi.org/10.7759/cureus.7255
  • Kummervold, P. E., Martin, S., Dada, S., Kilich, E., Denny, C., Paterson, P. y Larson, H. J. (2021). Categorizing Vaccine Confidence With a Transformer-Based Machine Learning Model: Analysis of Nuances of Vaccine Sentiment in Twitter Discourse. JMIR Medical Informatics, 9, https://doi.org/10.2196/29584
  • Larrondo-Ureta, A., Fernández, S.-P., & Morales-i-Gras, J. (2021). Desinformación, vacunas y Covid-19. Análisis de la infodemia y la conversación digital en Twitter. Revista Latina de Comunicación Social, 79, 1-18. https://doi.org/10.4185/RLCS-2021-1504
  • Loomba, Sahil, de Figueiredo, A., Piatek, S. J., de Graaf, K. y Larson, H. J. (2021). Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature Human Behaviour, 5, 337-348. https://doi.org/10.1038/s41562-021-01056-1
  • López-Martín, Á., Gómez-Calderón, B. y Córdoba-Cabús, A. (2021). Desinformación y verificación de datos. El caso de los bulos sobre la vacunación contra la Covid-19 en España. Revista Ibérica de Sistemas e Tecnologias de Informação, 431-443.
  • MacCartney, B. (2009). Natural language inference. Stanford University. https://bit.ly/3qsAtla
  • Mantzarlis, A. (2018). Fact-checking 101. 85-100. En Ireton, Cherilyn, y Posetti, Julie (eds). Journalism, ‘fake news’ and disinformation: Handbook for journalism education and training. Paris: Unesco. https://bit.ly/3pAWizZ
  • Martín, A., Huertas-Tato, J., Huertas-García, Á., Villar-Rodríguez, G. y Camacho, D. (2021). FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference. arXiv preprint arXiv:2110.14532. https://bit.ly/32xKfux
  • Morel, A.P.M. (2021). Negationism of the COVID-19 and popular health education: to beyond the necropolitics. Trabalho, Educação e Saúde, 19. https://doi.org/10.1590/1981-7746-sol00315
  • Nowak, S. A., Chen, C., Parker, A. M., Gidengil, C. A. y Matthews, L. J. (2020). Comparing covariation among vaccine hesitancy and broader beliefs within Twitter and survey data. PloS One, 15. https://doi.org/10.1371/journal.pone.0239826
  • Pérez-Escolar, M. y Noguera-Vivo, J.M. (eds.) (2022). Hate speech and polarization in participatory society. Routledge. https://bit.ly/3FE8EwB
  • Saby D. et al. (2021) Twitter Analysis of COVID-19 Misinformation in Spain. En Mohaisen D., Jin R. (eds.) Computational Data and Social Networks. CSoNet 2021. Lecture Notes in Computer Science, 13116. Springer. https://doi.org/10.1007/978-3-030-91434-9_24
  • Salaverría, R., Buslón, N., López-Pan, F., León, B., López-Goñi, I. y Erviti, M.C. (2020). Desinformación en tiempos de pandemia: tipología de los bulos sobre la COVID-19. El Profesional de la Información, 29. https://doi.org/10.3145/epi.2020.may.15
  • Serrano-Puche, J. (2021). Digital desinformation and emotions: exploring the social risks of affective polarization. International Review of Sociology. 31, 231-245. https://10.1080/03906701.2021.1947953
  • Subbaraman, N. (2021). This COVID-vaccine designer is tackling vaccine hesitancy-in churches and on Twitter. Nature, 377-377. https://doi.org/10.1038/d41586-021-00338-y
  • Shahi, G.; Dirkson, A. y Majchrzak, T. (2021): An exploratory study of COVID-19 misinformation on Twitter. Online Social Networks and Media, 22, https://doi.org/10.1016/j.osnem.2020.100104
  • Surian, D., Nguyen, D. Q., Kennedy, G., Johnson, M., Coiera, E. y Dunn, A. G. (2016). Characterizing Twitter discussions about HPV vaccines using topic modeling and community detection. Journal of Medical Internet Research, 18. https://doi.org/10.2196/jmir.6045
  • Thelwall, M., Kousha, K. y Thelwall, S. (2021). COVID-19 vaccine hesitancy on English-language Twitter. El Profesional de la información, 30. https://doi.org/10.3145/epi.2021.mar.12
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N. y Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 5998-6008. https://bit.ly/3pFbpYY
  • Yardi, S. y Boyd, D. (2010). Dynamic debates: An analysis of group polarization over time on twitter. Bulletin of Science, Technology & Society, 30, 316-327. https://doi.org/10.1177%2F0270467610380011
  • Zhou, X., Coiera, E., Tsafnat, G., Arachi, D., Ong, M. S. y Dunn, A. G. (2015). Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter. MEDINFO https://bit.ly/3mGQ2ow
  • Zucker, Jane R. et al., (2020). Consequences of Undervaccination — Measles Outbreak, New York City, 2018–2019. The New England Journal of Medicine. 382, 1009-1017. https://doi.org/10.1056/NEJMoa1912514
Data source: Dialnet