Expresiones de la ética científica en Twitter/X: un análisis de co-ocurrencia de hashtags
Resumen
Esta investigación tiene un objetivo doble: primero, determinar en qué medida los temas de la ética científica y la integridad académica se han expresado en Twitter/X; segundo, revelar y profundizar en los tópicos que se generar sobre estas temáticas centrales y, con ello, analizar la dinámica de los principales usuarios que se insertan en éstas. Para tal fin, se conformó una base de datos con poco más de 201 mil tweets que usaron al menos uno de 22 términos definidos y relacionados con los temas principales. La unidad fundamental de análisis fueron los hashtags que se usan dentro de los tweets. Además, aplicamos el enfoque del análisis de redes sociales para revelar y entender cómo están conectados entre sí. Los resultados muestran que el tema de la ética científica está bien expresado en Twitter; también, que se han usado una gran diversidad de hashtags, que dan forman a diferentes ejes temáticos. Adicionalmente, los usuarios tienen distintos roles asociados a los hashtags. Se concluye demostrando que Twitter es un buen espacio para la expresión de la ética científica; sin embargo, existen vertientes tanto positivas como negativas al respecto.
Palabras clave
Ética en la investigación, Integridad académica, Twitter, Hashtags, Análisis de Redes Sociales, Inteligencia artificialCitas
Agazzi, E. (2018). Philosophy of science and ethics. Axiomathes, 28(6), 587–602. https://doi.org/10.1007/s10516-018-9408-0
Aguilar-Gallegos, N., Klerkx, L., Romero-García, L. E., Martínez-González, E. G., & Aguilar-Ávila, J. (2022). Social network analysis of spreading and exchanging information on Twitter: the case of an agricultural research and education centre in Mexico. The Journal of Agricultural Education and Extension, 28(1), 115–136. https://doi.org/10.1080/1389224X.2021.1915829
Aguilar-Gallegos, N., Martínez-González, E. G., & Aguilar-Ávila, J. (2017). Análisis de redes sociales: Conceptos clave y cálculo de indicadores. Universidad Autónoma Chapingo (UACh), Centro de Investigaciones Económicas, Sociales y Tecnológicas de la Agroindustria y la Agricultura Mundial (CIESTAAM). Serie: Metodologías y herramientas para la investigación, Volumen 5.
Aguilar-Gallegos, N., Valdés-López, A., Muñoz-Rodríguez, M., Martínez-González, E. G., Santoyo-Cortés, H., & Aguilar-Ávila, J. (2019). Análisis de redes en Twitter para la inserción en comunidades: el caso de un producto agroindustrial. Interciencia, 44(2), 75–81. https://www.interciencia.net/volumen-44-2019/volumen-44-numero-02/
ALLEA. (2023). The European Code of Conduct for Research Integrity – Revised Edition 2023. https://doi.org/10.26356/ECOC
Amato, F., Cozzolino, G., Moscato, F., & Xhafa, F. (2019). Semantic analysis of social data streams. In F. Xhafa, L. Barolli, & M. Greguš (Eds.), Advances in Intelligent Networking and Collaborative Systems (pp. 59–70). Springer International Publishing. https://doi.org/10.1007/978-3-319-98557-2_6
Amiri, M., Yaghtin, M., & Sotudeh, H. (2024). How do tweeters feel about scientific misinformation: an infoveillance sentiment analysis of tweets on retraction notices and retracted papers. Scientometrics, 129(1), 261–287. https://doi.org/10.1007/s11192-023-04871-7
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. ICWSM.
Bik, H. M., & Goldstein, M. C. (2013). An introduction to social media for scientists. PLoS Biology, 11(4), e1001535. https://doi.org/10.1371/journal.pbio.1001535
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10008(10), 1–12. https://doi.org/10.1088/1742-5468/2008/10/P10008
Bolland, M. J., Avenell, A., & Grey, A. (2025). Publication integrity: what is it, why does it matter, how it is safeguarded and how could we do better? Journal of the Royal Society of New Zealand, 55(2), 267–286. https://doi.org/10.1080/03036758.2024.2325004
Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269. https://doi.org/10.1016/S0378-8733(96)00301-2
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. SAGE Publications Limited.
Corvol, P. (2017). L’intégrité scientifique : de l’entre-soi à une approche systémique [Scientific integrity: from self-isolation to a systemic approach]. Médecine/Sciences, 33(8–9), 689–690. https://doi.org/10.1051/medsci/20173308001
Csárdi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.
Daer, A. R., Hoffman, R., & Goodman, S. (2014). Rhetorical functions of hashtag forms across social media applications. SIGDOC 2014 - Proceedings of the 32nd Annual International Conference on the Design of Communication. https://doi.org/10.1145/2666216.2666231
Del Fresno García, M., Daly, A. J., & Supovitz, J. (2015). Desvelando climas de opinión por medio del Social Media Mining y Análisis de Redes Sociales en Twitter. El caso de los Common Core State Standards. REDES. Revista Hispana Para El Análisis de Redes Sociales, 26(1), 53–76. https://doi.org/10.5565/rev/redes.531
Del-Fresno-García, M. (2014). Haciendo visible lo invisible: visualización de la estructura de las relaciones en red en Twitter por medio del análisis de redes sociales. El Profesional de La Información, 23(3), 246–252. https://doi.org/10.3145/epi.2014.may.04
Dellière, S. (2022). Les réseaux sociaux au service de l’intégrité scientifique, une arme à double tranchant [Social networks in the service of scientific integrity, a double-edged sword]. Médecine/Sciences, 38(5), 477–479. https://doi.org/10.1051/medsci/2022061
El Bairi, K., Fourtassi, M., El Fatimy, R., & El Kadmiri, N. (2023). Distance education as a tool to improve researchers’ knowledge on predatory journals in countries with limited resources: the Moroccan experience. International Journal for Educational Integrity, 19(1), 1–15. https://doi.org/10.1007/s40979-023-00122-7
Golder, S., Ahmed, S., Norman, G., & Booth, A. (2017). Attitudes toward the ethics of research using social media: A systematic review. Journal of Medical Internet Research, 19(6), e195. https://doi.org/10.2196/jmir.7082
Gross, C. (2016). Scientific misconduct. Annual Review of Psychology, 67, 693–711. https://doi.org/10.1146/annurev-psych-122414-033437
Grudniewicz, A., Moher, D., Cobey, K. D., Bryson, G. L., Cukier, S., Allen, K., Ardern, C., Balcom, L., Barros, T., Berger, M., Ciro, J. B., Cugusi, L., Donaldson, M. R., Egger, M., Graham, I. D., Hodgkinson, M., Khan, K. M., Mabizela, M., Manca, A., … Lalu, M. M. (2019). Predatory journals: no definition, no defence. Nature, 576(7786), 210–212. https://doi.org/10.1038/d41586-019-03759-y
Hanneman, R. A., & Riddle, M. (2011). Concepts and measures for basic network analysis. In J. Scott & P. J. Carrington (Eds.), The SAGE Handbook of Social Network Analysis (pp. 340–369). SAGE Publications Ltd.
Haunschild, R., & Bornmann, L. (2021). Can tweets be used to detect problems early with scientific papers? A case study of three retracted COVID-19/SARS-CoV-2 papers. Scientometrics, 126(6), 5181–5199. https://doi.org/10.1007/s11192-021-03962-7
Johnson, D. E. (1998). Métodos multivariados aplicados al análisis de datos. International Thompson Editores S.A. de C.V.
Liu, X. Z., & Fang, H. (2017). What we can learn from tweets linking to research papers. Scientometrics, 111(1), 349–369. https://doi.org/10.1007/s11192-017-2279-0
Lucio-Mendiola, A. Y., Martínez-González, E. G., Aguilar-Gallegos, N., Aguilar-Ávila, J., & Altamirano-Cárdenas, J. R. (2022). Presencia de productos orgánicos en Twitter desde la perspectiva del análisis de redes sociales. REDES. Revista Hispana Para El Análisis de Redes Sociales, 33(1), 43–60. https://doi.org/10.5565/rev/redes.933
Memon, N., Xu, J. J., Hicks, D. L., & Chen, H. (2010). Social network data mining: Research questions, techniques, and applications. In N. Memon, J. J. Xu, D. L. Hicks, & H. Chen (Eds.), Data Mining for Social Network Data. Annals of Information Systems, vol 12. (pp. 1–7). Springer US. https://doi.org/10.1007/978-1-4419-6287-4_1
Menapace, M. (2019). Scientific ethics: A new approach. Science and Engineering Ethics, 25(4), 1193–1216. https://doi.org/10.1007/s11948-018-0050-4
Pérez López, C. (2004). Técnicas de análisis multivariante de datos. Pearson Educación S.A.
Pilař, L., Stanislavská, L. K., Gresham, G., Poláková, J., & Rojík, S. (2018). Questionnaire vs. social media analysis - Case study of organic food. AGRIS On-Line Papers in Economics and Informatics, 10(3), 93–101. https://doi.org/10.7160/aol.2018.1003308
Pilař, L., Stanislavská, L. K., Kvasnička, R., Bouda, P., & Pitrová, J. (2021). Framework for social media analysis based on hashtag research. Applied Sciences (Switzerland), 11(8), 3697. https://doi.org/10.3390/app11083697
Pilař, L., Stanislavská, L. K., Rojík, S., Kvasnička, R., Poláková, J., & Gresham, G. (2018). Customer experience with organic food: Global view. Emirates Journal of Food and Agriculture, 30(11), 918–926. https://doi.org/10.9755/ejfa.2018.v30.i11.1856
Power, B. J. (2022). How to use Twitter at a scientific conference. MSphere, 7(3), 1–6. https://doi.org/10.1128/msphere.00121-22
Ravi, S., Zhou, A. E., Sloan, B., Bercovitch, L., & Grant-Kels, J. M. (2025). Navigating the ethics of predatory journals and processing fees in dermatology. Clinics in Dermatology, 43(4), 559–562. https://doi.org/10.1016/j.clindermatol.2024.12.019
Resnik, D. B. (1998). The ethics of science: an introduction. Routledge. https://www.routledge.com/The-Ethics-of-Science-An-Introduction/Resnik/p/book/9780415166980?srsltid=AfmBOoo2-GQav75qYuRfGHaSmYtirSRkYdd1r4iVFsj7Ad9xJETVXpPD
Rocha, A., & Smart, P. (2021). Social media and the rise of predatory journals: A case report. Medical Writing, 30(1), 46–51.
Romero, D. M., Meeder, B., & Kleinberg, J. (2011). Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter. Proceedings of the 20th International Conference on World Wide Web, WWW 2011, 695–704. https://doi.org/10.1145/1963405.1963503
Rout, L., Khilar, P. P., & Rout, B. (2023). Does YouTube promote research ethics and conduct? A content analysis of Youtube Videos and analysis of sentiments through viewers comments. Accountability in Research, 31(8), 1024–1043. https://doi.org/10.1080/08989621.2023.2192404
Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5, 269–287. https://doi.org/10.1016/0378-8733(83)90028-X
Smith, M. A. (2013). NodeXL: Simple network analysis for social media. 2013 International Conference on Collaboration Technologies and Systems (CTS), 89–93. https://doi.org/10.1109/CTS.2013.6567211
Smith, M. A., Shneiderman, B., Milic-Frayling, N., Mendes Rodrigues, E., Barash, V., Dunne, C., Capone, T., Perer, A., & Gleave, E. (2009). Analyzing (social media) networks with NodeXL. Proceedings of the Fourth International Conference on Communities and Technologies - C&T ’09, 255–264. https://doi.org/10.1145/1556460.1556497
Sorokowski, P., Kulczycki, E., Sorokowska, A., & Pisanski, K. (2017). Predatory journals recruit fake editor. Nature, 543(7646), 481–483. https://doi.org/10.1038/543481a
Talmadge, J. E. (2020). A discussion on considerations in scientific integrity. International Immunopharmacology, 81, 21–22. https://doi.org/10.1016/j.intimp.2020.106332
Tanimoto, T., Kami, M., & Shibuya, K. (2013). Research misconduct and scientific integrity: A call for a global forum. The Lancet, 382(9896), 940. https://doi.org/10.1016/S0140-6736(13)61933-9
Teixeira da Silva, J. A., & Dobránszki, J. (2019). A new dimension in publishing ethics: social media-based ethics-related accusations. Journal of Information, Communication and Ethics in Society, 17(3), 354–370. https://doi.org/10.1108/JICES-05-2018-0051
Terry, P. E. (2019). Untangling truth, facts, and opinions: Why simple tenets of science have become uneasy. American Journal of Health Promotion, 33(3), 337–341. https://doi.org/10.1177/0890117119831864
Van Noorden, R. (2014). Online collaboration: Scientists and the social network. Nature, 512(7513), 126–129. https://doi.org/10.1038/512126a
Wasserman, S., & Faust, K. (1994). Social Network Analysis: methods and applications. Cambridge University Press.
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Zappavigna, M. (2011). Ambient affiliation: A linguistic perspective on Twitter. New Media and Society, 13(5), 788–806. https://doi.org/10.1177/1461444810385097
Publicado
Cómo citar
Descargas
Derechos de autor 2026 Norman Aguilar-Gallegos, Leticia Elizabeth Romero-García, Rómulo Ramírez-Daza y García
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
