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 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.

IntroductionThis article aims to investigate the potential impact of restricted social data access on digital research practices. The 2018 Cambridge Analytica scandal exposed the exploitation of Facebook user data for speculative purposes and led to the end of the so-called “Data Golden Age,” characterized by free access to social media user data. As a result, many social platforms have limited or entirely banned data access. This policy shift, referred to as the “APIcalypse,” has revolutionized digital research methods.MethodsTo address the impact of this policy shift on digital research, a non-probabilistic sample of Italian researchers was surveyed and the responses were analyzed. The survey was designed to explore how constraints on digital data access have altered research practices, whether we are truly in a post-API era with a radical change in data scraping strategies, and what shared and sustainable solutions can be identified for the post-API scenario.ResultsThe findings highlight how limits on social data access have not yet created a “post-Api” scenario as expected, but it is turning research practices upside down, positively and negatively. On the positive side, because researchers are experimenting with innovative forms of scraping. Negatively, because there could be a “mass migration” to the few platforms that freely grant their APIs, with critical consequences for the quality of research.DiscussionThe closure of many social media APIs has not opened up a post-API world, but has worsened the conditions of making research, which is increasingly oriented to “easy-data” environments such as Twitter. This should prompt digital researchers to make a self-reflexive effort to diversify research platforms and especially to act ethically with user data. It would also be important for the scientific world and large platforms to enter into understandings for open and conscious sharing of data in the name of scientific progress.

No abstract

No abstract