Digital Mixed Content Analysis for the Study of Digital Platform Social Data: An Illustration from the Analysis of COVID-19 Risk Perception in the Italian Twittersphere.

Abstract

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.