One of the main functions of public health is to monitor population health to identify health problems and priorities. Social media is increasingly being used to promote it. This study aims to investigate the field of diabetes and obesity and related tweets in the context of health and disease. The database extracted using academic APIs (Application Programming Interfaces) allowed the study to be run with content analysis and sentiment analysis techniques. These two analysis techniques are some of the tools of choice for the intended objectives. Content analysis facilitated the representation of a concept and a connection between two or more concepts, such as diabetes and obesity, on a purely text-based social platform such as Twitter. Sentiment analysis therefore allowed us to explore the emotional aspect related to the collected data related to the representation of such concepts. The results show a variety of representations connected to the two concepts and their correlations. From them it was possible to produce some clusters of elementary contexts and structure narrative and representational dimensions of the investigated concepts. The use of sentiment analysis and content analysis and cluster output to represent complex contexts such as diabetes and obesity for a social media community could increase knowledge of how virtual platforms impact fragile categories, facilitating concrete spillovers into public health strategies.

(1) Background: The importance of sport in advancing the roadmap for the implementation of the UN 2030 Agenda was reaffirmed at the 7th Summit on Smart Cities and Sport (Lausanne, 26–28 October 2020), where it was emphasized that developing a culture of sustainability among younger generations is crucial. Given this scenario, the present study focuses on two main research questions: “Is there a diffusion of an environmentally oriented culture among university students?” and “Is there a relationship between sports’ world and environmentally oriented culture?” (2) Methods: To establish the empirical framework (279 questionnaires received), we used web-based survey techniques with a Computer-Assisted Web Interviewing (CAWI) procedure. (3) Results: The analysis results show that the sample considers environmental sustainability as a part of a more comprehensive understanding of sustainability, which reflects a sense of fear for uncertainty in the future. The second finding suggests that sport does not play a significant role in shaping awareness. (4) Conclusions: The cultural and generational background must be considered for better understanding of the research results, especially in Italy where it appears that sustainability is not considered a priority.

This chapter examines the transformation of European healthcare systems in the context of digitalization and datafication. Drawing on sociological theories of digital infrastructures, welfare transformation, and digital inequalities, the chapter analyzes how digital health systems reshape access to healthcare data and the conditions of digital health citizenship. Using data from the European Commission's Digital Decade eHealth Composite Indicator, the study develops a comparative analysis of healthcare data infrastructures across European countries. The findings show that European health systems differ significantly in the integration of healthcare data ecosystems and in the inclusiveness of data access policies. These differences generate distinct institutional configurations of digital health citizenship. The chapter argues that digitalization does not simply expand access to healthcare information but also produces new forms of infrastructural inequality in data-driven welfare systems.

European welfare systems are undergoing a profound transformation driven by the integration of digital technologies, and artificial intelligence into public administration. This shift reflects processes of datafication, through which welfare increasingly operates as a sociotechnical system mediated by algorithms and digital platforms. While these innovations promise greater efficiency, predictive capacity, and resource optimization, they also raise concerns about opacity, surveillance, and inequality in access to social rights. The chapter examines an often-overlooked dimension of welfare digitalization: citizens' perceptions of artificial intelligence. Drawing on comparative data from Eurobarometer, it analyzes how attitudes toward AI vary across European countries. The findings reveal that public perceptions are structured around three main dimensions: technological optimism, concerns about automation-related job risks, and perceived digital skills. The results also highlight significant cross-national differences linked to institutional trust and socio-economic conditions.