CNR Author
operator:
and / or
Typology
operator:
and / or
Language
operator:
and / or
Date
operator:
and / or
Rights
operator:
and / or
2022
Journal article
Open Access
Understanding peace through the world news
Voukelatou V., Miliou I., Giannotti F., Pappalardo L.
Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country's profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace.Source: EPJ 11 (2022). doi:10.1140/epjds/s13688-022-00315-z
DOI: 10.1140/epjds/s13688-022-00315-z
Project(s): XAI
, SoBigData-PlusPlus
Metrics:
Voukelatou V., Miliou I., Giannotti F., Pappalardo L.
Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country's profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace.Source: EPJ 11 (2022). doi:10.1140/epjds/s13688-022-00315-z
DOI: 10.1140/epjds/s13688-022-00315-z
Project(s): XAI
, SoBigData-PlusPlus Metrics:
See at:
epjdatascience.springeropen.com 
| ISTI Repository | CNR ExploRA
2020
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
International Journal of Data Science and Analytics | link.springer.com | International Journal of Data Science and Analytics | ISTI Repository | CNR ExploRA
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
Metrics:
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
Metrics:
See at:
doi.org 
| ieeexplore.ieee.org | CNR ExploRA