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Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning
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 ind...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619768/ https://www.ncbi.nlm.nih.gov/pubmed/37910443 http://dx.doi.org/10.1371/journal.pone.0292604 |
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author | Liebovitch, Larry S. Powers, William Shi, Lin Chen-Carrel, Allegra Loustaunau, Philippe Coleman, Peter T. |
author_facet | Liebovitch, Larry S. Powers, William Shi, Lin Chen-Carrel, Allegra Loustaunau, Philippe Coleman, Peter T. |
author_sort | Liebovitch, Larry S. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10619768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-106197682023-11-02 Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning Liebovitch, Larry S. Powers, William Shi, Lin Chen-Carrel, Allegra Loustaunau, Philippe Coleman, Peter T. PLoS One Research Article 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. Public Library of Science 2023-11-01 /pmc/articles/PMC10619768/ /pubmed/37910443 http://dx.doi.org/10.1371/journal.pone.0292604 Text en © 2023 Liebovitch et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Liebovitch, Larry S. Powers, William Shi, Lin Chen-Carrel, Allegra Loustaunau, Philippe Coleman, Peter T. Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning |
title | Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning |
title_full | Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning |
title_fullStr | Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning |
title_full_unstemmed | Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning |
title_short | Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning |
title_sort | word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619768/ https://www.ncbi.nlm.nih.gov/pubmed/37910443 http://dx.doi.org/10.1371/journal.pone.0292604 |
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