  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.
