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Language is both a cause and a consequence of the social processes that leadto conflict or peace. Hate speech can mobilize violence and destruction. Whatare the characteristics of peace speech that reflect and support the socialprocesses that maintain peace? This study used existing peace indices, machinelearning, and on-line, news media sources to identify the words most associatedwith lower-peace versus higher-peace countries. As each peace index measuresdifferent social properties, there is little consensus on the numerical valuesof these indices. There is however greater consensus with these indices for thecountries that are at the extremes of lower-peace and higher-peace. Therefore,a data driven approach was used to find the words most important indistinguishing lower-peace and higher-peace countries. Rather than assuming atheoretical framework that predicts which words are more likely in lower-peaceand higher-peace countries, and then searching for those words in news media,in this study, natural language processing and machine learning were used toidentify the words that most accurately classified a country as lower-peace orhigher-peace. Once the machine learning model was trained on the wordfrequencies from the extreme lower-peace and higher-peace countries, that modelwas also used to compute a quantitative peace index for these and otherintermediate-peace countries. The model successfully yielded a quantitativepeace index for intermediate-peace countries that was in between that of thelower-peace and higher-peace, even though they were not in the training set.This study demonstrates how natural language processing and machine learningcan help to generate new quantitative measures of social systems, which in thisstudy, were linguistic differences resulting in a quantitative index of peacefor countries at different levels of peacefulness.

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