The present study is focused on the online debate relating to the Brexit process, three years and half since the historical referendum that has sanctioned the divide of the United Kingdom from the European Union. In our analysis we consider a corpus of approximately 33 million Brexit related tweets, shared on Twitter for 58 weeks, spanning from 31 December 2019 to 9 February 2020. Due to its great accessibility to data, Twitter constitutes a convenient data source to monitor and evaluate a wide variety of topics. In addition, Twitter's marked orientation towards news and the dissemination of information makes this microblogging network more connected to politics compared to other platforms. Through static and dynamic topic modelling techniques, we were able to identify the topics that have attracted the most attention from Twitters users and to characterise their temporal evolution. The topics retrieved by the static model highlight the major events of the Brexit process while the dynamic analysis recovered the persistent themes of discussion and debate over the entire period.

The expression ‘open data’ relates to a system of informative and freely accessible databases that public administrations make generally available online in order to develop an informative network between institutions, enterprises and citizens. On this topic, using the semantic network analysis method, the research aims to investigate the communication structure and the governance of open data in the Twitter conversational environment. In particular, the research questions are: (1) Who are the main actors in the Italian open data infrastructure? (2) What are the main conversation topics online? (3) What are the pros and cons of the development and use (reuse) of open data in Italy? To answer these questions, we went through three research phases: (1) analysing the communication network, we found who are the main influencers; (2) once we found who were the main actors, we analysed the online content in the Twittersphere to detect the semantic areas; (3) then, through an online focus group with the main open data influencers, we explored the characteristics of Italian open data governance. Through the research, it has been shown that: (1) there is an Italian open data governance strategy; (2) the Italian civic hacker community plays an important role as an influencer; but (3) there are weaknesses in governance and in practical reuse.

Due to COVID-19, higher education institutions all over the world transitioned to online learning. The sudden and forced transition to this new learning methodology pushed the Universities to rapidly adequate to the needs, upgrading their digital platforms to comply with the new requirements. In the same way, teachers had to adapt their teaching to fit the new medium’s potentials and limitations. The final receivers of this striking change, the students, had to adequate to the novelty approach, though this process has not been painless. Several difficulties, challenges and opportunities arose in this transition process for students, and the full digital class delivery also stressed them emotively. This study explores University of Foggia students’ perceptions of the emergency online learning. The factors analyzed involved their perception about the University implementation of the online class delivery, their consideration about the future of online learning and their emotional impact in attending courses with this methodology. Quantitative and qualitative data were collected from 3,140 participants. The findings present how students have globally appreciated online learning, but they mostly prefer a blended learning approach. Furthermore, by using segmentation variables, differences emerged among the participants’ groups, indicating online learning can have great potentials, but more complex and integrated approaches are needed to fulfill the different learning needs.

Over the last years, the prodigious success of online social media sites has marked a shift in the way people connect and share information. Coincident with this trend is the proliferation of location-aware devices and the consequent emergence of user-generated geospatial data. From a social scientific perspective, these location data are of incredible value as it can be mined to provide researchers with useful information about activities and opinions across time and space. However, the utilization of geo-located data is a challenging task, both in terms of data management and in terms of knowledge production, which requires a holistic approach. In this paper, we implement an integrated knowledge discovery in cyberspace framework for retrieving, processing and interpreting Twitter geolocated data for the discovery and classification of the latent opinion in user-generated debates on the internet. Text mining techniques, supervised machine learning algorithms and a cluster spatial detection technique are the building blocks of our research framework. As real-word example, we focus on Twitter conversations about Brexit, posted on Uk during the 13 months before the Brexit day. The experimental results, based on various analysis of Brexit-related tweets, demonstrate that different spatial patterns can be identified, clearly distinguishing pro- and anti-Brexit enclaves and delineating interesting Brexit geographies.

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