Echo chambers and vaccines against COVID-19 mis/disinformation on Twitter: machine learning and network analysis-based approach
DOI:
https://doi.org/10.33448/rsd-v12i2.40159Keywords:
Vaccine; Infodemics; Fake News; Echo Chambers; Twitter; Machine learning.Abstract
Given the infodemiological importance of echo chambers in the dissemination of mis/disinformation, we aimed to analyze the interaction networks of users most exposed to mis/disinformation or controversy about vaccines in the context of the COVID-19 pandemic. To this end, a methodology based on machine learning and Social Network Analysis is proposed in this research for automated detection of controversial and mis/disinformative content about vaccines, through which a model with 92% accuracy was achieved. Out of the nearly 24 million tweets collected, 12.4 million (52%) were flagged as controversial and/or potential for mis/disinformation, and the months of January and June 2021 were those with the highest activity, being analyzed through a cohort. Unlike previous work, we analyzed the network of all ways of interacting on Twitter, and the entire textual structure of the tweets - not just links or hashtags -. Regarding the conversation about COVID-19 vaccines, the findings were different from those associated with party-political discussion previously described in the literature, since the network of mentions and replies privileges heterophilic relationships, and "echo" conformations were not observable. Finally, further studies are needed to better understand the dissemination of misinformation about vaccines on Twitter.
References
Amaral I, Santos SJ (2019) Algoritmos e redes sociais: a propagação de fake News na era da pós-. In: Figueira J, Santos S (Ed.), As fake news e a nova ordem (des)informativa na era da pós-verdade (pp. 63–85). Portugal: Imprensa da Universidade de Coimbra.
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). doi:https://doi.org/10.1038/S41598-020-73510-5
Cohen, K. B., & Hunter, L. (2008). Getting Started in Text Mining. PLOS Computational Biology, 4(1), e20. doi:https://doi.org/10.1371/JOURNAL.PCBI.0040020
Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Menczer, F., & Flammini, A. (2011). Political Polarization on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 89–96. doi:https://doi.org/10.1609/ICWSM.V5I1.14126
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv. doi: https://doi.org/10.48550/arxiv.1810.04805
Du, S., & Gregory, S. (2017). The echo chamber effect in twitter: Does community polarization increase? Studies in Computational Intelligence, 693, 373–384. doi:https://doi.org/10.1007/978-3-319-50901-3_30
Eysenbach, G. (2009). Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. Journal of Medical Internet Research, 11(1). doi:https://doi.org/10.2196/JMIR.1157
Ferreira, F. V., Varão, R., Boselli, M. A., Santos, L. B., & Moret, M. A. (2022). Uso de Python para detecção de fake news sobre a covid-19: desafios e possibilidades. Revista Eletrônica de Comunicação, Informação & Inovação Em Saúde, 16(2). doi:https://doi.org/10.29397/RECIIS.V16I2.3253
Ferreira-Mello, R., André, M., Pinheiro, A., Costa, E., & Romero, C. (2019). Text mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(6), e1332. doi:https://doi.org/10.1002/WIDM.1332
Geron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. Tokyo: O'Reilly.
Gomaa, W., & A. Fahmy, A. (2013). A Survey of Text Similarity Approaches. International Journal of Computer Applications, 68(13), 13–18. doi:https://doi.org/10.5120/11638-7118
Hotho, A., Nürnberger, A., & Paaß, G. (2005). A Brief Survey of Text Mining. Journal for Language Technology and Computational Linguistics, 20(1), 19–62. doi:https://doi.org/10.21248/JLCL.20.2005.68
Jung, H., & Lee, B. G. (2020). Research trends in text mining: Semantic network and main path analysis of selected journals. Expert Systems with Applications, 162, 113851. doi:https://doi.org/10.1016/J.ESWA.2020.113851
Khan, J. Y., Khondaker, Md. T. I., Afroz, S., Uddin, G., & Iqbal, A. (2021). A benchmark study of machine learning models for online fake news detection. Machine Learning with Applications, 4, 100032. doi:https://doi.org/10.1016/J.MLWA.2021.100032
Lima, C. R. M. de, Sánchez-Tarragó, N., Moraes, D., Grings, L., & Maia, M. R. (2020). Emergência de saúde pública global por pandemia de Covid-19. Folha de Rosto, 6(2), 5–21. doi:https://doi.org/10.46902/2020N2P5-21
Liu.B. (2020). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge, England: Cambridge University Press.
Macanovic, A. (2022). Text mining for social science – The state and the future of computational text analysis in sociology. Social Science Research, 108, 102784. doi:https://doi.org/10.1016/J.SSRESEARCH.2022.102784
Massarani, L., Brotas, A., Costa, M., & Neves, LF. (2021). Vacinas contra a COVID-19 e o combate à desinformação na cobertura da Folha de S. Paulo. Fronteiras - Estudos Midiáticos, 23(2), 29–43. doi:https://doi.org/10.4013/fem.2021.232.03
Mønsted, B., & Lehmann, S. (2022). Characterizing polarization in online vaccine discourse—A large-scale study. PLOS ONE, 17(2), e0263746. doi:https://doi.org/10.1371/JOURNAL.PONE.0263746
Recuero, R. (2017). Introdução à análise de redes sociais online. Salvador: EDUFBA.
Recuero, R., & Soares, F. B. (2021). O Discurso Desinformativo sobre a Cura do COVID-19 no Twitter: Estudo de caso. E-Compós, 24, 1–29. doi:https://doi.org/10.30962/EC.2127
Recuero, R., & Zago, G. (2021). “RT, por favor”: considerações sobre a difusão de informações no Twitter. Fronteiras - Estudos Midiáticos, 12(2), 69–81. doi:https://doi.org/10.4013/4668
Santos, C. R. P. dos, & Maurer, C. (2020). Potencialidades e limites do fact-checking no combate à desinformação. Comunicação & Informação, 23. doi:https://doi.org/10.5216/CI.V23I.57839
Shore, J., Baek, J., & Dellarocas, C. (2018, October 22). Twitter Is Not the Echo Chamber We Think It Is. MITSloan Management Review. https://sloanreview.mit.edu/article/twitter-is-not-the-echo-chamber-we-think-it-is/
Soares, F. B., Viegas, P., Bonoto, C., & Recuero, R. (2021). Covid-19, desinformação e Facebook: circulação de URLs sobre a hidroxicloroquina em páginas e grupos públicos. Galáxia (São Paulo), 46. doi:https://doi.org/10.1590/1982-2553202151423
Suen, C. Y. (1979). n-Gram Statistics for Natural Language Understanding and Text Processing. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 164–172. doi:https://doi.org/10.1109/TPAMI.1979.4766902
Tao, D., Yang, P., & Feng, H. (2020). Utilization of text mining as a big data analysis tool for food science and nutrition. Comprehensive Reviews in Food Science and Food Safety, 19(2), 875–894. doi:https://doi.org/10.1111/1541-4337.12540
Törnberg, P. (2018). Echo chambers and viral misinformation: Modeling fake news as complex contagion. PLoS ONE, 13(9). doi:https://doi.org/10.1371/JOURNAL.PONE.0203958
van der Linden, S. (2022). Misinformation: susceptibility, spread, and interventions to immunize the public. Nature Medicine, 28(3), 460–467. doi:https://doi.org/10.1038/s41591-022-01713-6
World Health Organization. (2020). 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
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Arthur da Silva Lopes; Antonio Brotas
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.