Authorized Twitter voices during the COVID-19 pandemic: actors, vocabulary, and feelings as an interpretive framework for ordinary users

Authors

DOI:

https://doi.org/10.35669/rcys.2020.10(2).549-568

Keywords:

Confinement, coronavirus, COVID-19, gender, government, health, health crisis, media, pandemic, twitter

Abstract

This work addresses the communicative role of authorized voices on Twitter during the COVID-19 pandemic and their interaction with ordinary users. They are defined as public profile users who have a big number of followers, and whose messages are massively disseminated on the platform by ordinary users. A set of tweets was collected over two months through the Twitter API, and then a subset of data was formed with the tweets replicated more than 100 times. Labeling, data mining, and sentiment analysis techniques were applied to it. It is observed that the interpretive framework of the pandemic is modeled by the media, although there are perceptions of ordinary users about the pandemic as a time of economic, health, political and personal crisis that are not present in the authorized voices. It is concluded that the media and front-line government officials are the ones that achieved the greatest adherence and amplification of the word by ordinary users, although there is a significant gender gap between the voices of men and those of women.

Downloads

Download data is not yet available.

Author Biographies

Gabriela Elisa Sued Palmeiro, Tecnologico de Monterrey

She holds a PhD in Humanistic Studies with a specialization in Cultural Studies of Science and Technology from the Monterrey Institute of Technology. She holds a Master in Science, Technology and Society (Universidad Nacional de Quilmes), and a Bachelor of Arts (Universidad de Buenos Aires). She is a professor at ITESM, Mexico, and before that she was a professor in the Social Communication Sciences Department at the University of Buenos Aires. Her teaching and research topics are: digital research methods, the social uses of information and communication technologies, digital cultures, and the relationships between gender, science and technological innovation.

Manuel Cebral Loureda, Tecnologico de Monterrey

PhD and BA in Philosophy from the University of Santiago de Compostela with a thesis on data mining and Big Data. He holds a Master's degree in Statistical Learning and Data Mining from the UNED and another in Art, Philosophy and Creativity from the University of Valencia. He is currently a full time professor at ITESM, Mexico, where he coordinates the Line of Generation and Application of Knowledge (LGAC) of Science, Technology and Society as well as the Digital Humanities Seminar. His teaching and research topics revolve around the social impact of technologies, the philosophy of technology and posthumanism, implementing computational methods of computation through tools such as R programming.

References

Burgess, J., & Baym, N. K. (2020). Twitter: A biography. New York University Press.

Calvo, E., & Aruguete, N. (2020). Fake news, trolls y otros encantos: Cómo funcionan. Buenos Aires: Siglo XXI Editores.

Camarena, M. E., Saavedra, M. L., & Saldívar, D. D. (2015). Panorama del género en México: Situación actual. Revista Guillermo de Ockham, 13(2), 77-87. doi: 10.21500/22563202.2066

Castells, M. (2012). Redes de indignación y esperanza: Los movimientos sociales en la era de internet. Alianza Editorial.

Cebral Loureda y Sued Palmeiro (2020) La percepción de COVID-19 en Twitter. Análisis computacional de la conversación pública en lengua española. En proceso de publicación

Ceciarini, S. (2019). Women in Politics: Local and European Trends. The Council of European Municipalities and Regions (CEMR).

Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data: Political Homophily on Twitter. Journal of Communication, 64(2), 317-332. doi: 10.1111/jcom.12084

Csárdi, G. (2019). Package ‘igraph’. Network Analysis and Visualization. https://cran.r-project.org/package=igraph

De Miguel, R. de M., Hanitzsch, T., Fernández, S. P., & Conde, M. R. B. (2017). Mujeres periodistas en España: Análisis de las características sociodemográficas y de la brecha de género. El profesional de la información, 26(3), 497-506. http://www.elprofesionaldelainformacion.com/contenidos/2017/may/16_esp.pdf

El Universal. (2020). ¿Cómo es el consumo digital en México en el marco de la pandemia? El Universal. Recuperado de https://www.eluniversal.com.mx/techbit/como-es-el-consumo-digital-en-mexico-en-el-marco-de-la-pandemia

Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., Wang, Y., Fu, H., & Dai, J. (2020). Mental health problems and social media exposure during COVID-19 outbreak. PLOS ONE, 15(4), e0231924. doi: 10.1371/journal.pone.0231924

Frankze, A., Bechmann, A., Zimmer, M., & Ess, C. (s. f.). Internet Research: Ethical Guidelines 3.0.

Han, X., Wang, J., Zhang, M., & Wang, X. (2020). Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China. International Journal of Environmental Research and Public Health, 17(8). doi: 10.3390/ijerph17082788

Kearney, M. (2020). Package ‘RTweet’. https://cran.r-project.org/web/packages/rtweet/rtweet.pdf

Kullar, R., Goff, D. A., Gauthier, T. P., & Smith, T. C. (2020). To Tweet or Not to Tweet—A Review of the Viral Power of Twitter for Infectious Diseases. Current Infectious Disease Reports, 22(6), 14. https://doi.org/10.1007/s11908-020-00723-0

Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a Social Network or a News Media?. Proceedings of the 19th International Conference on World Wide Web, 591–600. doi: 10.1145/1772690.1772751

Lin Pedersen, T (2020a). Package ‘ggraph’. https://ggraph.data-imaginist.com

Lin Pedersen, T. (2020b). Package ‘tidygraph’. A Tidy API for Graph Manipulation. Recuperado de https://cran.r-project.org/package=tidygraph

Mohammad, S., Kiritchenko, S., & Zhu, X. (2013). NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets. ArXiv: 1308.6242

Moreno, A., & Redondo, T. (2016). Text Analytics: The convergence of Big Data and Artificial Intelligence. International Journal of Interactive Multimedia and Artificial Intelligence, 3 (Special Issue on Big Data and AI, 6), 57-64. doi: 10.9781/ijimai.2016.369

Newman, N., Fletcher, R., Schulz, A., Simge, A., & Kleis Nielsen, C. (2020). Reuters Institute Digital News Report 2020. Reuters Institute for the Study of Journalism. Recuperado de http://www.digitalnewsreport.org

Percastre-Mendizábal, S., Pont-Sorribes, C., & Suau-Gomila, G. (2019). La gestión comunicativa en redes sociales digitales de la emergencia del Ébola en España. The communicative management in social media of the emergence of Ebola in Spain., 80-90. doi: 10.20318/recs.2019.4437

Robinson, D. (2020). Package ‘widyr’. Widen, process, and re-tidy a dataset. https://cran.r-project.org/package=widyr

Rogers, Richard. (2018). Otherwise Engaged: Social Media from Vanity Metrics to Critical Analytics. International Journal of Communication, 12(0), 23. Recuperado de https://ijoc.org/index.php/ijoc/article/view/6407

Roy, M., Moreau, N., Rousseau, C., Mercier, A., Wilson, A., & Atlani-Duault, L. (2020). Ebola and Localized Blame on Social Media: Analysis of Twitter and Facebook Conversations During the 2014–2015 Ebola Epidemic. Culture, Medicine, and Psychiatry, 44(1), 56-79. doi: 10.1007/s11013-019-09635-8

Rufai, S. R., & Bunce, C. (2020). World leaders’ usage of Twitter in response to the COVID-19 pandemic: A content analysis. Journal of Public Health, 42(3), 510-516. doi: 10.1093/pubmed/fdaa049

Robinson, D., & Silge, J. (2020). Package ‘tidytext’. Text Mining using «dplyr», «ggplot2», and Other Tidy Tools (0.2.4). Recuperado de https://cran.r-project.org/package=tidytext

Thelwall, M., & Thelwall, S. (2020). Covid-19 tweeting in English: Gender differences. El Profesional de La Información, 29(3). doi: 10.3145/epi.2020.may.01

Towers, S., Afzal, S., Bernal, G., Bliss, N., Brown, S., Espinoza, B., Jackson, J., Judson-Garcia, J., Khan, M., Lin, M., Mamada, R., Moreno, V. M., Nazari, F., Okuneye, K., Ross, M. L., Rodriguez, C., Medlock, J., Ebert, D., & Castillo-Chavez, C. (2015). Mass Media and the Contagion of Fear: The Case of Ebola in America. PLOS ONE, 10(6), e0129179. doi: 10.1371/journal.pone.0129179

Vega Montiel, A. (2014). Igualdad de género, poder y comunicación: Las mujeres en la propiedad, dirección y puestos de toma de decisión. Revista de Estudios de Género La Ventana, 5(40), 186-212.

Victoria-Mas, M. (2020). Report: News consumption patterns and misinformation perceptions during the COVID-19 crisis in Spain. CharleMagne Prize Academy. Recuperado de https://www.charlemagneprizeacademy.com/en/publications/report-news-consumption-patterns-and-misinformation-perceptions-during-the-covid-19-crisis-in-spain

Wickham, H. (2019). Package ‘tidyverse’. https://cran.r-project.org/package=tidyverse

Published

2020-11-14

How to Cite

Sued Palmeiro, Gabriela Elisa, and Manuel Cebral Loureda. 2020. “Authorized Twitter voices during the COVID-19 pandemic: actors, vocabulary, and feelings as an interpretive framework for ordinary users”. Revista de Comunicación y Salud 10 (2):549-68. https://doi.org/10.35669/rcys.2020.10(2).549-568.