A Mixed Content Analysis Design in the Study of the Italian Perception of Covid-19 on Twitter

Abstract

The digital era and the boom of social, user-generated and freely available and usable content on the Net has brought to the fore a classic technique, accused too often of being highly subjective and requiring a large amount of intellectual work. This technique is Content Analysis, which has seen an unprecedented explosion in recent years. In addition to the incessant flow, speed of diffusion and high volume of today’s big data, the attention of social researchers – as well as of anyone interested in drawing information from this enormous proliferation of data – is shifting towards new possibilities. Among these we find that of having a notion of the contents conveyed, of the feelings expressed, of the polarities of big data, but also the chance to extract other information that indirectly speaks of the tastes, opinions, beliefs and transformations behind the behavior of the users of the Net. In fact, secondary data available on the Net, collectable through sophisticated query systems with API or with web scraping software, make it possible to accumulate huge amounts of this dense social data, from which it is possible to try to extract not only trends but real knowledge, in a quantitative as well as in a qualitative manner. This enriches the value of the results that can be produced with Content Analysis and limits, until disappearing, all the critical horizons that have classically left this technique in the shadows, allowing it to find new applicative dignity, validity and reliability (Hamad et al. 2016). In order to explain this evidence, the contribution that we will present attempts to prove that the return of Content Analysis techniques is not only due to the change in the scenario and in the data analyzed, but also to the ability of this technique to innovate and evolve, leading to open analytical perspectives beyond contingent changes. This can be demonstrated through the application of digital mixed content analysis to the recent Covid-19 outbreak and its development of the perception of the Italian population on a specific digital social platform, Twitter. Keywords: Digital Mixed Content Analysis Model, digital platform social data, Twitter, Italy, coronavirus.