Measuring well-being through novel digital data

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 Ph.D. thesis 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 offcial 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.