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

TL;DR

This paper uses natural language processing and machine learning to identify words associated with peace levels in countries, creating a quantitative peace index based on news media language.

Contribution

It introduces a data-driven method to classify countries' peace levels using news media language, producing a new quantitative peace index.

Findings

The model accurately distinguished lower- and higher-peace countries.

It generated a meaningful peace index for intermediate-peace countries.

Linguistic features correlate with social peace levels.

Abstract

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, there is little consensus on the numerical values of these indices. 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.