# Understanding peacefulness through the world news

arXiv: https://arxiv.org/abs/2106.00306 · HTML: https://mlsanity.com/paper/2106.00306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 131 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Mean Absolute Percentage Error for regression models](https://mlsanity.com/paper/1605.02541/) (2016)
- [Sentiment Analysis in the News](https://mlsanity.com/paper/1309.6202/) (2013)
- [Hope Speech Detection: A Computational Analysis of the Voice of Peace](https://mlsanity.com/paper/1909.12940/) (2019)
- [Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data](https://mlsanity.com/paper/1409.2983/) (2014)
- [An analytical framework to nowcast well-being using mobile phone data](https://mlsanity.com/paper/1606.06279/) (2016)
- [A survey of results on mobile phone datasets analysis](https://mlsanity.com/paper/1502.03406/) (2015)
- [Socio-economic, built environment, and mobility conditions associated with crime: A study of multiple cities](https://mlsanity.com/paper/2004.05822/) (2020)
- [Detecting criminal organizations in mobile phone networks](https://mlsanity.com/paper/1404.1295/) (2014)
- [Application of Data Science to Discover Violence-Related Issues in Iraq](https://mlsanity.com/paper/2006.07980/) (2020)

## Abstract (arXiv)

Peacefulness 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 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 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 peacefulness.