Laura Fortunato

Reconstruction of a Real World Social Network using the Potts Model and Loopy Belief Propagation.

The scope of this paper is to test the adoption of a statistical model derived from Condensed Matter Physics, for the reconstruction of the structure of a social network. The inverse Potts model, traditionally applied to recursive observations of quantum states in an ensemble of particles, is here addressed to observations of the members states in an organization and their (anti)correlations, thus inferring interactions as links among the members. Adopting proper (Bethe) approximations, such an inverse problem is showed to be tractable. Within an operational framework, this network-reconstruction method is tested for a small real-world social network, the Italian parliament. In this study case, it is easy to track statuses of the parliament members, using (co)sponsorships of law proposals as the initial dataset. In previous studies of similar activity-based networks, the graph structure was inferred directly from activity co-occurrences: here we compare our statistical reconstruction with such standard methods, outlining discrepancies and advantages.

Sentiment Analysis for Government: An Optimized Approach

This paper describes a Sentiment Analysis SA method to analyze tweets polarity and to enable government to describe quantitatively the opinion of active users on social networks with respect to the topics of interest to the Public Administration. n nWe propose an optimized approach employing a document-level and a dataset-level supervised machine learning classifier to provide accurate results in both individual and aggregated sentiment classification. n nThe aim of this work is also to identify the types of features that allow to obtain the most accurate sentiment classification for a dataset of Italian tweets in the context of a Public Administration event, also taking into account the size of the training set. This work uses a dataset of 1,700 Italian tweets relating to the public event of Lecce 2019 --- European Capital of Culture.

Considerations About Multistep Community Detection

The problem and implications of community detection in networks have raised a huge attention, for its important applications in both natural and social sciences. A number of algorithms has been developed to solve this problem, addressing either speed optimization or the quality of the partitions calculated. In this paper we propose a multi-step procedure bridging the fastest, but less accurate algorithms (coarse clustering), with the slowest, most effective ones (refinement). By adopting heuristic ranking of the nodes, and classifying a fraction of them as `critical, a refinement step can be restricted to this subset of the network, thus saving computational time. Preliminary numerical results are discussed, showing improvement of the final partition.

An integrated path for supporting spin-off creation: Desirable features of University-Industry interface in the Triple Helix Model

This study examines the University-Industry (UI) interface, in Triple Helix (TH) Model. In the era of Knowledge-based Economy, the Third Mission generates new value that depending from UI interface. New businesses, revealed with the Spin-offs creation, catalyzed by this interface, aims to commercialize the results coming from research activities. The purpose of this article is to understand features of an UI interface in order to support adequately the spin-off establishment and reduce the risk of not overcome the Death Valley. The study was conducted through direct interviews to 39 CEOs of the University of Salento spin-offs. So, applying Social Network Analysis (SNA) and Sentiment Analysis (SA) techniques, four clusters of main emerged themes and the relative text sentiment were identified.

A Study on the Collective Perceived Representation of a Real Urban Area through the Usage of an Engaging Framework, Based on a 3D Virtual Environment and Openstreetmap Data

The need for urban regeneration does not come only by structural requirements, but also by socio- cultural needs. What we are going to propose is the urban regeneration as a way to perceive, in a different way, the surrounding spaces allowing users to receive and provide a wide range of information on the urban environment. Each space of a city has a variety of intrinsic meanings provided by human groups interacting with each other everyday. The purpose is collecting the hidden information thanks to citizens contribution. The objective is the involvement of citizens as builders of sense through a playful attitude as builders of virtual cities, and using game based on motivation as impetus for the regeneration. Urban regeneration is innovative thanks to a new participatory and cooperative methodology based on the perception of every citizen, and on the collection of players experiences.

Ethical design in ICT application: how satisfy food citizenship needs

A new food behaviour named Food citizenship brings to new market trends. Consumers, motivated by personal, environmental, economical and social interests, want to know more information about food, in order to make an awareness and sustainable choice. Leveraging on Industry 4.0 strategically guidelines, a system able to manage and share the food information along the Agrifood Value Chain, is easy to develop but probably goes to the detriment of human factor. The challenge is to communicate this information to the food citizen, intercepting, at the same time, their interests and their value. In order to find a strategy to show the food-product information using ICT technology, tweets about EXPO 2015 are analysed through Social Network and Semantic Analysis. A comparison with the XTC trends definitions allowed the conception and definition of an ethical communication model and IT solution, able to illustrate to customer the food-product in a pertinent way.

Cultural Event Management and Urban e-Planning Through Bottom-Up User Participation

Cultural events are an important driver of socio-cultural-economic transformation. The growth of Information and Communication Technologies (ICTs) has affected the ways in which people can play an active role in cultural event management and urban planning. This work proposes a methodological approach that identifies the key elements for building bottom-up urban e-planning strategies. After a brief theoretical analysis of the impact of cultural activities, tourism and ICTs on urban planning, this paper presents the results of an empirical study carried out in the Puglia region (south of Italy) during the cultural event known as “La Notte della Taranta”, in which the crowd created added-value information via comments posted on social media. Data were collected using a mobile application specifically created for this event as part of the Folkture project, as well as from Facebook and Twitter posts. Using network-analytic and sentiment/semantic algorithms, the work aims to support the event management decisional process and produce results valuable to the field of urban planning.

University-Industry Interface: Open and Dynamic Business Models to Reduce Spin-offs Risk of Failure

Abstract nFollowing the triple helix (TH) model and the way knowledge is transferred into the industry domain, this chapter aims to define features interface that should be implemented in order to facilitate the University–Industry (UI) relationship and thus encourage the spin-off creation. n nIn order to support this relationship, a new business model configuration of an entrepreneurial ecosystem is proposed, aiming at creating a sustainable environment, where business entities can grow. The field of the Governance of Entrepreneurial Ecosystems is also investigated in order to define a framework for launching, developing, and sustaining a company over time. n nThis chapter presents a case study developed within the University of Salento (Italy). It capitalizes results from three different research analyses, based on questionnaires and interviews with actors of the spin-off network (professors and researchers, graduating students, admin-tech staff of the Technology Transfer Office, spin-offs’ CEOs/Associates, and R&D managers of external companies) and on results coming from scientific publications and regional/national reports in the innovation context. n nA research methodology based on semantic network analysis and sentiment analysis has been applied in order to identify which features an interface should implement in order to facilitate the UI relationship and encourage the spin-off creation. n nTo support the start-up overcoming the “death valley,” the creation of a link between the strategy used to transfer value to the market and the phase of innovation is proposed inside the business model configuration. Some aspects of a governance model of an entrepreneurial ecosystem were also presented in order to support the business evolution of a single business entity and assuring sustainability over time.

Statistical mechanics approach for collaborative business social network reconstruction

The role of human resources has become a key factor for the success of an organization. Based on a research collaboration with an aeronautical company, the paper compares two different approaches for the reconstruction of a collaborative social network in the business realm. Traditional social network analysis and novel statistical inference models were both evaluated against data provided by the company, with the final scope of scouting key employees in the network, as well as exploiting the knowledge-transfer processes. As a main outcome of this paper, it was found how the network reconstruction using statistical models has an increased robustness, as well as sensitivity, allowing to discover hidden correlations among the users.

An approach from statistical mechanics for collaborative business social network reconstruction

The role of human resources has become a key factor for the success of an organization. Based on a research collaboration with an aeronautical company, the paper proposes a comparison of two different approaches for the reconstruction of a collaborative social network in the business realm: the use of traditional Social Network Analysis and novel statistical inference models. Both approaches were evaluated against data provided by the company, in order to scout the key people in the network and the knowledge-transfer processes. As a main outcome of this paper, it was found how the network reconstruction using statistical models has an increased robustness, as well as sensitivity, allowing to discover hidden correlations among the users.