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Analyzing public sentiment toward GMOs via social media between 2019-2021
Genetically modified organisms or GMOs offer significant advantages in food production, including increased yield, decreased pesticide usage, and better disease resistance. However, adoption and public sentiment toward GMOs is highly variable. Without positive sentiment toward GMOs, consumption of G...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10038016/ https://www.ncbi.nlm.nih.gov/pubmed/36947744 http://dx.doi.org/10.1080/21645698.2023.2190294 |
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author | Sohi, Manreet Pitesky, Maurice Gendreau, Joseph |
author_facet | Sohi, Manreet Pitesky, Maurice Gendreau, Joseph |
author_sort | Sohi, Manreet |
collection | PubMed |
description | Genetically modified organisms or GMOs offer significant advantages in food production, including increased yield, decreased pesticide usage, and better disease resistance. However, adoption and public sentiment toward GMOs is highly variable. Without positive sentiment toward GMOs, consumption of GMO-based foods may not have an adequate market for further investment. In order to better understand overall public sentiment toward GMO-based foods, a Boolean search was created using a commercial web-crawling service to collect and analyze public sentiment of GMOs across multiple social media and web-based services from May 1, 2019, to May 31, 2021. The Boolean query identified 2 million mentions of GMOs during the study period. Using the commercial software’s sentiment analysis (i.e. classifying mentions as either neutral, negative, or positive), 54% of the mentions were categorized as having a neutral sentiment, 32% as having a negative sentiment, and 14% as having a positive sentiment. Further emotional analysis (classifying posts by the emotion expressed, e.g., disgust, joy, sadness, anger, fear, surprise) produced by the software shows that the majority of the mentions were categorized as expressing a negative emotion: 31% of mentions expressed disgust, 28% joy, 18% sadness, 16% anger, 7% fear, and 1% surprise. Among the various social media sources collected, Twitter was the main source of data, providing 62% of the total 2 million mentions, followed by 14% from news sources and 12% from Reddit. These types of data can be used to better understand trends in sentiment toward GMOs and ultimately play an important role in combating mis-information. |
format | Online Article Text |
id | pubmed-10038016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-100380162023-03-25 Analyzing public sentiment toward GMOs via social media between 2019-2021 Sohi, Manreet Pitesky, Maurice Gendreau, Joseph GM Crops Food Research Article Genetically modified organisms or GMOs offer significant advantages in food production, including increased yield, decreased pesticide usage, and better disease resistance. However, adoption and public sentiment toward GMOs is highly variable. Without positive sentiment toward GMOs, consumption of GMO-based foods may not have an adequate market for further investment. In order to better understand overall public sentiment toward GMO-based foods, a Boolean search was created using a commercial web-crawling service to collect and analyze public sentiment of GMOs across multiple social media and web-based services from May 1, 2019, to May 31, 2021. The Boolean query identified 2 million mentions of GMOs during the study period. Using the commercial software’s sentiment analysis (i.e. classifying mentions as either neutral, negative, or positive), 54% of the mentions were categorized as having a neutral sentiment, 32% as having a negative sentiment, and 14% as having a positive sentiment. Further emotional analysis (classifying posts by the emotion expressed, e.g., disgust, joy, sadness, anger, fear, surprise) produced by the software shows that the majority of the mentions were categorized as expressing a negative emotion: 31% of mentions expressed disgust, 28% joy, 18% sadness, 16% anger, 7% fear, and 1% surprise. Among the various social media sources collected, Twitter was the main source of data, providing 62% of the total 2 million mentions, followed by 14% from news sources and 12% from Reddit. These types of data can be used to better understand trends in sentiment toward GMOs and ultimately play an important role in combating mis-information. Taylor & Francis 2023-03-22 /pmc/articles/PMC10038016/ /pubmed/36947744 http://dx.doi.org/10.1080/21645698.2023.2190294 Text en © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
spellingShingle | Research Article Sohi, Manreet Pitesky, Maurice Gendreau, Joseph Analyzing public sentiment toward GMOs via social media between 2019-2021 |
title | Analyzing public sentiment toward GMOs via social media between 2019-2021 |
title_full | Analyzing public sentiment toward GMOs via social media between 2019-2021 |
title_fullStr | Analyzing public sentiment toward GMOs via social media between 2019-2021 |
title_full_unstemmed | Analyzing public sentiment toward GMOs via social media between 2019-2021 |
title_short | Analyzing public sentiment toward GMOs via social media between 2019-2021 |
title_sort | analyzing public sentiment toward gmos via social media between 2019-2021 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10038016/ https://www.ncbi.nlm.nih.gov/pubmed/36947744 http://dx.doi.org/10.1080/21645698.2023.2190294 |
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