Word Differences in News Media of Lower and Higher Peace Countries Revealed by Natural Language Processing and Machine Learning - Abstract

Aug 4, 2026

Language shapes conflict and peace, with “hate speech” often fueling violence. This study examines what characterizes “peace speech” in online news media by using existing peace indices plus natural language processing and machine learning. It learns from word frequencies in extreme low- and high-peace countries, then classifies countries and generates a quantitative peace index for intermediate cases between extremes. Results support data-driven linguistic measures for social peacefulness.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Abstract

Introduction

Importance of Language

Measuring Peace

Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning Larry S. Liebovitch* (Queens College, City University of New York and Columbia University, USA) William Powers (Queens College, City University of New York, USA) Lin Shi (Queens College, City University of New York, USA) Allegra Chen-Carrel (University of San Francisco, USA) Philippe Loustaunau (Vista Consulting LLC, USA) Peter T. Coleman (Columbia University, USA) *corresponding author: email: [LSL2140@columbia.edu](LSL2140@columbia.edu) 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 lowerpeace 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 higherpeace 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 higherpeace, 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 indexof peace for countries at different levels of peacefulness. Introduction Importance of Language Communication through language has been highlighted as the single most important process in constructing our reality (Luhmann, 1987; Karlberg, 2011) . Language also plays a critical role in conflicts. The extreme power of “hate speech” to mobilize destruction and violence is evident around the globe. In Kenya, hate speech over social media and in blogs played a central role in inciting ethnic divides and conflict (Kimotho & Nyaga, 2016) . In Nigeria, hate speech in the news was identified as a major driver of election violence (Ezeibe, 2021) . Studies in Poland have shown that exposure to hate speech leads to lower evaluations of victims, greater distancing, and more outgroup prejudice (Soral, Bilewicz, & Winiewski, 2018) . Peacekeepers working in conflict zones are currently using data science and natural language processing methods to track hate speech – monitoring hostile news accounts, blogs, and broadcast and social media posts in order to provide early warning predictions of increases in ethnic tensions or violence in local communities (Peace Tech Lab, 2020) . These studies have focused on the prevention of destructive conflicts, approaching peace as the absence of harmful conflict. However, highly peaceful societies have been found to evidence other conditions and processes in addition to an absence of violence that distinguish them from low peace nations, including the prevalence of non-warring norms, values and rituals (Fry

FAQs

What is the research goal regarding language and peace?

To identify linguistic characteristics of “peace speech” that reflect and support social processes maintaining peace, contrasting them with “hate speech.”

How does the study use machine learning to assess peace?

It trains a model on word frequencies from extreme low-peace and high-peace countries, then uses the model to classify countries and compute a quantitative peace index.

Why are peace indices treated cautiously in the study?

Because each peace index measures different social properties and numerical values vary, with stronger consensus mainly for countries at the extremes.

Aug 4, 2026

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