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1.
PLoS One ; 18(11): e0292604, 2023.
Article in English | MEDLINE | ID: mdl-37910443

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, they can have different values for the same country. 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.


Subject(s)
Language , Natural Language Processing , Linguistics , Machine Learning , Social Conditions
2.
Am Psychol ; 76(7): 1113-1127, 2021 10.
Article in English | MEDLINE | ID: mdl-33180535

ABSTRACT

Despite good faith attempts by countless citizens, civil society, governments, and the international community, living in a sustainably peaceful community continues to be an elusive dream in much of our world. Among the challenges to sustaining peace is the fact that few scholars have studied enduringly peaceful societies, or have examined only narrow aspects of them, leaving our understanding of the necessary conditions, processes and policies fragmented, and deficient. This article provides a work-in-progress overview of a multidisciplinary, multimethod initiative, which aims to provide a holistic, evidence-based understanding of how peace can be sustained in societies. The Sustaining Peace Project, launched in 2014, uses complexity science as an integrative platform for synthesizing knowledge across disciplines, sectors and communities. This article introduces the multiple components of the project and shares preliminary findings. (PsycInfo Database Record (c) 2022 APA, all rights reserved).


Subject(s)
Social Conditions , Societies , Humans , Research Report
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