Resilience in online communities of practice during the COVID-19 pandemic: an Italian case study

ABSTRACT

This paper presents the results of a case study conducted through digital ethnography and textual analysis methods. The thematic focus concerns the role of online communities of practice as resilience tools in the case of emergency and crisis. The origin of this assumption lies in some reflections developed in view of the effects of the COVID-19 pandemic, especially given the varied typology of social responses to this crisis. A fundamental aim in this research is the exploration of the dynamics and spontaneous practices of social support that take place today in digital environments. This study uses the theoretical paradigm of community resilience and its interpretative dimensions, such as proactivity and adaptation. The analysis of the online community Noi Denunceremo - Verità e giustizia per le vittime del COVID-19 (We Will Denounce - Truth and Justice for the Victims of COVID-19) indicates the interdisciplinary relevance of pursuing this avenue of research, especially in view of the findings that identify the role of online communities of practice in promoting inclusive and participatory responses to emergencies and crises.

KEYWORDS:

• online community of practice
• resilience
• digital ethnography
• COVID-19

Disclosure statement

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

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1 As the heterogeneity of the data collected (from big data to data collected through interviews and participant observation) and the characteristics of the social phenomenon studied, the research naturally leads to the use of mixed methods integrated in a circular and flexible research logic to increase the reliability and validity of the results (Maretti, 2019). With regard to data collection and analysis, it is necessary to specify that the data collected, and the content acquired from the data mining phase, were acquired in compliance with the regulations in force, and were treated with the utmost respect in terms of the identity of the user, eliminating any possible connection or traceability between them and the content produced. The research group worked in line with the IRE3.0 deontological protocols (https://aoir.org/reports/ethics3.pdf) aimed at protecting the research field even, and especially, when the data are public and freely accessible (Maretti & Russo, 2019b).

2 Word clusters were identified using the modularity algorithm (Blondel et al., 2008).

Additional information

Notes on contributors

Mara Maretti

Mara Maretti, PhD, is professor of sociology and teaches Social Data Science: Theories and Applications and Social Policy at the G. D'Annunzio University of Chieti-Pescara, where she is the coordinator of the Computational Social Research Laboratory.

Vanessa Russo

Vanessa Russo, PhD, is Junior Researcher (PON REACT-EU) in the Department of Legal and Social Science at the Università G. d’Annunzio of Chieti-Pescara and member of Computational Social Research Laboratory. Barbara Lucini, PhD, is adjunct professor of Risk Management and Crisis Communication at Catholic University of Sacred Heart in Milan, Italy. As part of the Department of Sociology, she teaches courses in Public Policies.

Barbara Lucini

Barbara Lucini is phd in Sociology and Methodology of Social Research, Post Doc at the Department of Sociology, Catholic University of Sacred Heart, Milan and Senior Researcher at ITSTIME, Department of Sociology, Catholic University of Sacred Heart, Milan. She is adjunct professor of risk management and crisis communication at the Catholic University, Milan, Italy.