The explosion of platform social data as digital secondary data, collectable through sophisticated and automatized query systems or algorithms, makes it possible to accumulate huge amounts of dense and miscellaneous data. The challenge for social researchers becomes how to extract meaning and not only trends in a quantitative and in a qualitative manner. Through the application of a digital mixed content analysis design, we present the potentiality of a hybrid digitalized approach to social content applied to a very tricky question: the recognition of risk perception during the first phase of COVID-19 in the Italian Twittersphere. The contribution of our article to mixed methods research consists in the extension of the existing definitions of content analysis as a mixed approach by combining hermeneutic and automated procedures, and by creating a design model with vast application potential, especially when applied to the digital scenario.
The rare circumstances of COVID-19 have transformed research toward increased dependence on online spaces. This article examines related challenges and opportunities, focusing on how philosophical and ethical implications are differentially manifest amid crisis. Anchored by a transformative perspective, our framework recognizes heightened vulnerabilities amid COVID-19; it seeks dexterous strategies for implementing qualitative strands that adapt well to a virtual context while remaining philosophically grounded and ethical. Our findings highlight issues of unequal access, disembodiment, safety and vulnerability, researcher positionality, anonymity, and the delineation between private and public spaces; we also showcase an array of virtual qualitative methods. We conclude that ethical practice in the use of online methods is likely to be broadly applicable and adaptable to the mixed methods research community.
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