Giovani in un'ora - Ciclo di seminari - Seconda parte
- Day - Time: 11 May 2022, h.11:00
Vasiliki Voukelatou - "Measuring well-being through novel digital data"
Abstract: Well-being is an important value for people's lives, and it is crucial for societal progress. Considering that well-being is a vague and multi-dimensional concept, it cannot be captured as a whole but through a set of health, socio-economic, safety, environmental, and political dimensions. The current research presents and discusses well-being, its dimensions, and how AI tools and novel digital data help in capturing them. Also, our research focuses on the safety dimension, and in particular on peace, which is an emerging challenge nowadays. Peace is the way out of inequity and violence, and its measurement is crucial, considering that the world is constantly under socio-economic, political, and military instability. Novel digital data streams and AI tools foster peace studies during the last years. Following this direction, we exploit information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture the Global Peace Index (GPI), a well-known official peace index. Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a higher frequency than the official yearly index cost- and time-efficiently. Additionally, we conduct variable importance analysis, and we use explainable AI techniques to understand better the models' behaviour, peace, and its determinants. This in-depth analysis highlights each country's profile and explains the predictions, prediction errors, and events that drive these errors. We believe that novel digital data exploited by researchers, policymakers, and non-governmental organisations, with data science tools as powerful as machine learning, could maximize the societal benefits and minimize the risks to peace and well-being as a whole.
Salvatore Citraro - "Feature-rich Networks: When topology meets semantics"
Abstract: During the last decades, network science has become one of the fastest growing multidisciplinary research fields. Networks are the natural way to express phenomena whose unit elements exhibit complex interdependent organization. However, while reasoning on networks built on top of contextual data, topology is only one of the aspects to consider: nodes and edges often carry additional semantic information that are of uttermost importance to properly understand the phenomena expressed by the underlying topological structure. Feature-rich network models are growing to simultaneously merge networks and non-network information among attributes on nodes, categories of connections or dynamic features. In this brief overview I will explain why it is necessary to define novel algorithms and tools for feature-rich network mining, and how it is possible to extract knowledge that is invisible to structural-only network mining.