Estimating countries’ peace index through the lens of the world news as monitored by GDELT

Voukelatou, Vasiliki; Pappalardo, Luca; Miliou, Ioanna; Gabrielli, Lorenzo; Giannotti, Fosca

doi:10.1109/dsaa49011.2020.00034

Cited by 9 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nonetheless, with that understanding, this still provides a useful relative comparison between the machine learning and the traditional peace indices. Recently, other machine learning methods have been used to show that events from the GDELT (Global Data on Events, Locations, and Tone) digital news database [ 44 ] successfully correlates with, and can even predict, the values of the GPI over time [ 39 , 45 ]. Those studies used pre-assigned event categories, while our work here used machine learning to identify the words that differentiate lower and higher levels of peace without prior assumptions on what those words would be.…”

Section: Discussionmentioning

confidence: 99%

Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning

Liebovitch,

Powers,

Shi

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Language is both a cause and a consequence of the social processes that lead to conflict or peace. “Hate speech” can mobilize violence and destruction. What are the characteristics of “peace speech” that reflect and support the social processes that maintain peace? This study used existing peace indices, machine learning, and on-line, news media sources to identify the words most associated with lower-peace versus higher-peace countries. As each peace index measures different social properties, they can have different values for the same country. There is however greater consensus with these indices for the countries that are at the extremes of lower-peace and higher-peace. Therefore, a data driven approach was used to find the words most important in distinguishing lower-peace and higher-peace countries. Rather than assuming a theoretical framework that predicts which words are more likely in lower-peace and higher-peace countries, and then searching for those words in news media, in this study, natural language processing and machine learning were used to identify the words that most accurately classified a country as lower-peace or higher-peace. Once the machine learning model was trained on the word frequencies from the extreme lower-peace and higher-peace countries, that model was also used to compute a quantitative peace index for these and other intermediate-peace countries. The model successfully yielded a quantitative peace index for intermediate-peace countries that was in between that of the lower-peace and higher-peace, even though they were not in the training set. This study demonstrates how natural language processing and machine learning can help to generate new quantitative measures of social systems, which in this study, were linguistic differences resulting in a quantitative index of peace for countries at different levels of peacefulness.

show abstract

Section: Discussionmentioning

confidence: 99%

Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning

Liebovitch,

Powers,

Shi

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In this paper, which expands our previous studies [17,18], we produce GPI estimates from 1-month-ahead up to 6-months-ahead, conduct the analysis using additional machine learning models, and apply explainable AI techniques to analyze the behavior of high performance models in-depth. Furthermore, we include 12 more recent data points in our analysis, i.e., from April 2019 to March 2020.…”

Section: Introductionmentioning

confidence: 94%

Understanding peace through the world news

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Besides, GPI though GDELT is produced at a low cost, and in a time-efficient way, as compared to the traditional methodology. In this study we expand the approach introduced in [87].…”

Section: Introductionmentioning

confidence: 99%

Understanding peacefulness through the world news

Voukelatou¹,

Miliou²,

Giannotti³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Peacefulness is a principal dimension of wellbeing for all humankind and is the way out of inequity and every single form of violence. Thus, its measurement has lately drawn the attention of researchers and policymakers. During the last years, novel digital data streams have drastically changed the 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 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 the SHAP methodology to obtain the most important variables that drive the predictions. This analysis highlights each country's profile and provides explanations for the predictions overall, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by Social Good researchers, policy-makers, and peace-builders, with data science tools as powerful as machine learning, could contribute to maximize the societal benefits and minimize the risks to peacefulness.

show abstract

Estimating countries’ peace index through the lens of the world news as monitored by GDELT

Cited by 9 publications

References 43 publications

Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning

Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning

Understanding peace through the world news

Understanding peacefulness through the world news

Contact Info

Product

Resources

About