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Semantic Network Analysis Reveals Opposing Online Representations of the Search Term “GMO”

Making sound food and agriculture decisions is important for global society and the environment. Experts tend to view crop genetic engineering, a technology that can improve yields and minimize impacts on the environment, more favorably than the public. Because there is a causal relationship between...

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Autores principales: Jiang, Ke, Anderton, Brittany N., Ronald, Pamela C., Barnett, George A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6607347/
https://www.ncbi.nlm.nih.gov/pubmed/31565301
http://dx.doi.org/10.1002/gch2.201700082
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author Jiang, Ke
Anderton, Brittany N.
Ronald, Pamela C.
Barnett, George A.
author_facet Jiang, Ke
Anderton, Brittany N.
Ronald, Pamela C.
Barnett, George A.
author_sort Jiang, Ke
collection PubMed
description Making sound food and agriculture decisions is important for global society and the environment. Experts tend to view crop genetic engineering, a technology that can improve yields and minimize impacts on the environment, more favorably than the public. Because there is a causal relationship between public opinion and public policy, it is important to understand how opinions about genetically engineered (GE) crops are influenced. The public increasingly seeks science information on the Internet. Here, semantic network analysis is performed to characterize the presentation of the term “GMO (genetically modified organism),” a proxy for food developed from GE crops, on the web. Texts from three sources are analyzed: U.S. federal websites, top pages from a Google search, and online news titles. We found that the framing and sentiment (positive, neutral, or negative attitudes) of “GMO” varies across these sources. It is described how differences in the portrayal of GE food by each source might affect public opinion. A current understanding of the types of information individuals may encounter online can provide insight into public opinion toward GE food. In turn, this knowledge can guide teaching and communication efforts by the scientific community to promote informed decision‐making about agricultural biotechnologies.
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spelling pubmed-66073472019-09-27 Semantic Network Analysis Reveals Opposing Online Representations of the Search Term “GMO” Jiang, Ke Anderton, Brittany N. Ronald, Pamela C. Barnett, George A. Glob Chall Full Papers Making sound food and agriculture decisions is important for global society and the environment. Experts tend to view crop genetic engineering, a technology that can improve yields and minimize impacts on the environment, more favorably than the public. Because there is a causal relationship between public opinion and public policy, it is important to understand how opinions about genetically engineered (GE) crops are influenced. The public increasingly seeks science information on the Internet. Here, semantic network analysis is performed to characterize the presentation of the term “GMO (genetically modified organism),” a proxy for food developed from GE crops, on the web. Texts from three sources are analyzed: U.S. federal websites, top pages from a Google search, and online news titles. We found that the framing and sentiment (positive, neutral, or negative attitudes) of “GMO” varies across these sources. It is described how differences in the portrayal of GE food by each source might affect public opinion. A current understanding of the types of information individuals may encounter online can provide insight into public opinion toward GE food. In turn, this knowledge can guide teaching and communication efforts by the scientific community to promote informed decision‐making about agricultural biotechnologies. John Wiley and Sons Inc. 2017-12-27 /pmc/articles/PMC6607347/ /pubmed/31565301 http://dx.doi.org/10.1002/gch2.201700082 Text en © 2017 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Full Papers
Jiang, Ke
Anderton, Brittany N.
Ronald, Pamela C.
Barnett, George A.
Semantic Network Analysis Reveals Opposing Online Representations of the Search Term “GMO”
title Semantic Network Analysis Reveals Opposing Online Representations of the Search Term “GMO”
title_full Semantic Network Analysis Reveals Opposing Online Representations of the Search Term “GMO”
title_fullStr Semantic Network Analysis Reveals Opposing Online Representations of the Search Term “GMO”
title_full_unstemmed Semantic Network Analysis Reveals Opposing Online Representations of the Search Term “GMO”
title_short Semantic Network Analysis Reveals Opposing Online Representations of the Search Term “GMO”
title_sort semantic network analysis reveals opposing online representations of the search term “gmo”
topic Full Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6607347/
https://www.ncbi.nlm.nih.gov/pubmed/31565301
http://dx.doi.org/10.1002/gch2.201700082
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