The construction of the meanings of #coronavirus on Twitter: An analysis of the initial reactions of the Italian people

ABSTRACT

The first months of 2020 saw the coronavirus pandemic explode. Moving from China, it arrived in Europe and hit Italy. The place where the debate around it exploded was the media ecosystem. In a short time, it was an explosion of tweets related to the hashtag #coronavirus on Twitter. With the aim of reconstructing the meanings of the hashtag and the content, in terms of sentiment and opinions, of the reactions of the Italians, we collected in a large size corpus, the hundred thousand Italian tweets containing the #coronavirus produced during the media hype period from the Twitter repository (February 24th - 28th, 2020). Media hype period was discovered by digging in the online articles of ‘la Repubblica', based on the presence of the words: coronavirus and Italy. The media hype is February 26th. The corpus underwent Emotional Text Mining (ETM), an unsupervised methodology, which allows social profiling based on communication. The study of the word chosen to talk about a topic and their co-occurrence allows the understanding of people’s symbolizations, representations, and sentiment, about the coronavirus. In a retrospective logic, this mechanism allows us to reconstruct the sensemaking and nuances of meaning attributed by users to the coronavirus hashtag.

KEYWORDS:

• Coronavirus
• Emotional Text Mining
• hashtag studies
• public debate
• Twitter

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The data extraction was carried out with the rtweet package of R (v.0.7.0; Kearney, 2020) providing an interface to the Twitter web API.

2 TTR = 0.008; Hapax% = 36.3.

3 Score = $\sum {\chi }_{i,j}^2{\displaystyle \frac{n_j}{N_k}}$ – χ² = chi square value of term (i) in the document (j) classified in the cluster (k); n_j = number of terms in the document (j) classified in the cluster (k); N_k = number of terms in cluster (k) (Lancia, 2018).

4 The percentage values were calculated using the absolute values of the number of tweets grouped for each sub-cluster.

Additional information

Notes on contributors

Giovanni Boccia Artieri

Giovanni Boccia Artieri (Ph.D.) is Full Professor in Sociology of Communication and Digital Media and Dean at the Dept. of Communication Sciences, Humanities and International Studies, University of Urbino Carlo Bo. He is Coordinator of the Ph.D program on Humanities. His main research interests revolve around media theory, with a focus on social media and participatory culture. He has published many articles in national and international Journals such as Information, Communication & Society, Current Sociology, International Journal of Communication, Participations. Journal of Audience & Reception Studies.

Francesca Greco

Francesca Greco (Ph.D.), NSH as Associate Professor in General Sociology, is Post Doc at the Department of Educational Science, University of Roma Tre; Lecturer at the Department of Communication and Social Research, Sapienza University of Rome; R&D manager of Prisma S.r.l. She is representative on organ donation for the Scientific Board of the Research Network in Sociology of Health and Medicine, Italian Sociological Association. She developed the Emotional Text Mining method that was awarded by the University Paris Descartes (USPC). She is an expert in social science research methods, computational sociology, big data, text mining, health sociology, and disability studies. On these topics she has written several papers and books for national and international journals and editors.

Gevisa La Rocca

Gevisa La Rocca (Ph.D.) is Associate Professor of Sociology of Communication at the University Kore of Enna, she is a member of the Scientific Council of the Processes and Cultural Institutions Group of the Italian Association of Sociology. She has a background in sociology, communication, quantitative and qualitative research methods. Her research interests cover communication research, hashtags studies, textual data analysis, risk studies. On these topics she has written several articles for national and international journals (Sage Open, Revue Internationale De Sociologie, Barataria. Revista Castellano-Manchega de Ciencias Sociales) and has written or was co-editor of books (Technological and Digital Risk: Research Issues, with J. Martínez-Torvisco, eds., Peter Lang, 2020).