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Examining Homophily, Language Coordination, and Analytical Thinking in Web-Based Conversations About Vaccines on Reddit: Study Using Deep Neural Network Language Models and Computer-Assisted Conversational Analyses

BACKGROUND: Vaccine hesitancy has been deemed one of the top 10 threats to global health. Antivaccine information on social media is a major barrier to addressing vaccine hesitancy. Understanding how vaccine proponents and opponents interact with each other on social media may help address vaccine h...

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Detalles Bibliográficos
Autores principales: Li, Yue, Gee, William, Jin, Kun, Bond, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131607/
https://www.ncbi.nlm.nih.gov/pubmed/36951921
http://dx.doi.org/10.2196/41882
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author Li, Yue
Gee, William
Jin, Kun
Bond, Robert
author_facet Li, Yue
Gee, William
Jin, Kun
Bond, Robert
author_sort Li, Yue
collection PubMed
description BACKGROUND: Vaccine hesitancy has been deemed one of the top 10 threats to global health. Antivaccine information on social media is a major barrier to addressing vaccine hesitancy. Understanding how vaccine proponents and opponents interact with each other on social media may help address vaccine hesitancy. OBJECTIVE: We aimed to examine conversations between vaccine proponents and opponents on Reddit to understand whether homophily in web-based conversations impedes opinion exchange, whether people are able to accommodate their languages to each other in web-based conversations, and whether engaging with opposing viewpoints stimulates higher levels of analytical thinking. METHODS: We analyzed large-scale conversational text data about human vaccines on Reddit from 2016 to 2018. Using deep neural network language models and computer-assisted conversational analyses, we obtained each Redditor’s stance on vaccines, each post’s stance on vaccines, each Redditor’s language coordination score, and each post or comment’s analytical thinking score. We then performed chi-square tests, 2-tailed t tests, and multilevel modeling to test 3 questions of interest. RESULTS: The results show that both provaccine and antivaccine Redditors are more likely to selectively respond to Redditors who indicate similar views on vaccines (P<.001). When Redditors interact with others who hold opposing views on vaccines, both provaccine and antivaccine Redditors accommodate their language to out-group members (provaccine Redditors: P=.044; antivaccine Redditors: P=.047) and show no difference in analytical thinking compared with interacting with congruent views (P=.63), suggesting that Redditors do not engage in motivated reasoning. Antivaccine Redditors, on average, showed higher analytical thinking in their posts and comments than provaccine Redditors (P<.001). CONCLUSIONS: This study shows that although vaccine proponents and opponents selectively communicate with their in-group members on Reddit, they accommodate their language and do not engage in motivated reasoning when communicating with out-group members. These findings may have implications for the design of provaccine campaigns on social media.
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spelling pubmed-101316072023-04-27 Examining Homophily, Language Coordination, and Analytical Thinking in Web-Based Conversations About Vaccines on Reddit: Study Using Deep Neural Network Language Models and Computer-Assisted Conversational Analyses Li, Yue Gee, William Jin, Kun Bond, Robert J Med Internet Res Original Paper BACKGROUND: Vaccine hesitancy has been deemed one of the top 10 threats to global health. Antivaccine information on social media is a major barrier to addressing vaccine hesitancy. Understanding how vaccine proponents and opponents interact with each other on social media may help address vaccine hesitancy. OBJECTIVE: We aimed to examine conversations between vaccine proponents and opponents on Reddit to understand whether homophily in web-based conversations impedes opinion exchange, whether people are able to accommodate their languages to each other in web-based conversations, and whether engaging with opposing viewpoints stimulates higher levels of analytical thinking. METHODS: We analyzed large-scale conversational text data about human vaccines on Reddit from 2016 to 2018. Using deep neural network language models and computer-assisted conversational analyses, we obtained each Redditor’s stance on vaccines, each post’s stance on vaccines, each Redditor’s language coordination score, and each post or comment’s analytical thinking score. We then performed chi-square tests, 2-tailed t tests, and multilevel modeling to test 3 questions of interest. RESULTS: The results show that both provaccine and antivaccine Redditors are more likely to selectively respond to Redditors who indicate similar views on vaccines (P<.001). When Redditors interact with others who hold opposing views on vaccines, both provaccine and antivaccine Redditors accommodate their language to out-group members (provaccine Redditors: P=.044; antivaccine Redditors: P=.047) and show no difference in analytical thinking compared with interacting with congruent views (P=.63), suggesting that Redditors do not engage in motivated reasoning. Antivaccine Redditors, on average, showed higher analytical thinking in their posts and comments than provaccine Redditors (P<.001). CONCLUSIONS: This study shows that although vaccine proponents and opponents selectively communicate with their in-group members on Reddit, they accommodate their language and do not engage in motivated reasoning when communicating with out-group members. These findings may have implications for the design of provaccine campaigns on social media. JMIR Publications 2023-03-23 /pmc/articles/PMC10131607/ /pubmed/36951921 http://dx.doi.org/10.2196/41882 Text en ©Yue Li, William Gee, Kun Jin, Robert Bond. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.03.2023. 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 work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Li, Yue
Gee, William
Jin, Kun
Bond, Robert
Examining Homophily, Language Coordination, and Analytical Thinking in Web-Based Conversations About Vaccines on Reddit: Study Using Deep Neural Network Language Models and Computer-Assisted Conversational Analyses
title Examining Homophily, Language Coordination, and Analytical Thinking in Web-Based Conversations About Vaccines on Reddit: Study Using Deep Neural Network Language Models and Computer-Assisted Conversational Analyses
title_full Examining Homophily, Language Coordination, and Analytical Thinking in Web-Based Conversations About Vaccines on Reddit: Study Using Deep Neural Network Language Models and Computer-Assisted Conversational Analyses
title_fullStr Examining Homophily, Language Coordination, and Analytical Thinking in Web-Based Conversations About Vaccines on Reddit: Study Using Deep Neural Network Language Models and Computer-Assisted Conversational Analyses
title_full_unstemmed Examining Homophily, Language Coordination, and Analytical Thinking in Web-Based Conversations About Vaccines on Reddit: Study Using Deep Neural Network Language Models and Computer-Assisted Conversational Analyses
title_short Examining Homophily, Language Coordination, and Analytical Thinking in Web-Based Conversations About Vaccines on Reddit: Study Using Deep Neural Network Language Models and Computer-Assisted Conversational Analyses
title_sort examining homophily, language coordination, and analytical thinking in web-based conversations about vaccines on reddit: study using deep neural network language models and computer-assisted conversational analyses
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131607/
https://www.ncbi.nlm.nih.gov/pubmed/36951921
http://dx.doi.org/10.2196/41882
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