2 result(s)
Page Size: 10, 20, 50
Export: bibtex, xml, json, csv
Order by:

CNR Author operator: and / or
Typology operator: and / or
Language operator: and / or
Date operator: and / or
Rights operator: and / or
2020 Journal article Open Access OPEN

Measuring objective and subjective well-being: Dimensions and data sources
Voukelatou V., Gabrielli L., Miliou I., Cresci S., Sharma R., Tesconi M., Pappalardo L.
Well-being is an important value for people's lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development.Source: International Journal of Data Science and Analytics (Print) (2020). doi:10.1007/s41060-020-00224-2
DOI: 10.1007/s41060-020-00224-2
Project(s): SoBigData via OpenAIRE

See at: link.springer.com Open Access | International Journal of Data Science and Analytics Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted


2020 Conference article Embargo

Estimating countries' peace index through the lens of the world news as monitored by GDELT
Voukelatou V., Pappalardo L., Miliou I., Gabrielli L., Giannotti F.
Peacefulness is a principal dimension of well-being, and its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, to capture peacefulness through the Global Peace Index (GPI). Applying machine learning techniques, we demonstrate that news media attention, sentiment, and social stability from GDELT can be used as proxies for measuring GPI at a monthly level. Additionally, through the variable importance analysis, we show that each country's socio-economic, political, and military profile emerges. This could bring added value to researchers interested in "Data Science for Social Good", to policy-makers, and peacekeeping organizations since they could monitor peacefulness almost real-time, and therefore facilitate timely and more efficient policy-making.Source: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 216–225, 06/10/2020, 09/10/2020
DOI: 10.1109/dsaa49011.2020.00034
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | doi.org Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | xplorestaging.ieee.org Restricted