The mobility of university students in Italy has been framed as a phenomenon linked to so-called intellectual migrations and as a subset of the historical and consolidated internal migration path explained in terms of South–North trajectory. This study describes the most important mobility trajectories of students across macro-areas and disciplinary fields, and then evaluates, using a multilevel logistic regression model, the factors that encouraged student cohort, who were enrolled in a degree program in the academic years 2014–2015, to move elsewhere from the Campania region. Beyond fixed and interaction effects related to the students’ personal characteristics, the model included possible random effects linked to the high schools attended by the students to capture the possible influence of the local context on migration choices.

In this work, we reconstruct the tweet-retweet and tweet-reply relations of opinions about a trending topic on the Twitter platform. We propose a multi-steps approach to derive a signed network expressing the spread of contents and opinions. The first step consists in reducing data dimensionality by means of a clustering procedure on tweets able to identify the concepts they express. In the second step, focusing on message contents, we adapt different sentiment analysis algorithms in order to determine the sign of both the original tweet (with respect to the trending topic) and the sign of the edge connecting the original tweet to the replies, conditional on the replied tweet. Each tweet will spread its concepts by means of signed retweet and reply relations. The aim is to study the different structure, in terms of both network structure and sentiment, of the signed network related to each concept. A comparative analysis will be possible as well among the various identified signed networks.

In the last years, there has been a growing interest in the significance of scientific collaboration. Several studies have analyzed co-authorship networks as a convenient tool to investigate collaboration among scientists across different disciplines and over time. Co-authorship networks among scientists can be represented by hypergraphs, where each scientific paper is the corresponding hyperevent. To account for this scenario and evaluate the evolution of the co-authorship network over time, Relational Hyperevent Models (RHEM) have been recently proposed. Such models can handle time-varying data and events that occur in hyperedges (a set of subsets of vertices in a hypergraph that can connect more than two variables), allowing modeling events composed of any authors, analyze polyadic settings, and consider subgroup persistence over time. This paper aims to apply such models to analyze the evolution of collaboration among the Italian academic statisticians, affiliated to five scientific sub-sectors, observed from 2012 to 2022, including their external co-authors. Bibliographic information has been retrieved from the Scopus database. Different model specifications are proposed to evaluate the tendency to maintain collaboration between authors over time (familiarity effects) and/or the tendency to create new collaborations with authors who co-authored with common groups of authors (closure effects).