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Diffusion size and structural virality: The effects of message and network features on spreading health information on twitter
Relying on diffusion of innovation theory, this study examines the impacts of perceived message features and network characteristics on size (i.e., the number of retweets a message receives) and structural virality (i.e., quantified distinction between broadcast and viral diffusion) of information d...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7127591/ https://www.ncbi.nlm.nih.gov/pubmed/32288177 http://dx.doi.org/10.1016/j.chb.2018.07.039 |
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author | Meng, Jingbo Peng, Wei Tan, Pang-Ning Liu, Wuyu Cheng, Ying Bae, Arram |
author_facet | Meng, Jingbo Peng, Wei Tan, Pang-Ning Liu, Wuyu Cheng, Ying Bae, Arram |
author_sort | Meng, Jingbo |
collection | PubMed |
description | Relying on diffusion of innovation theory, this study examines the impacts of perceived message features and network characteristics on size (i.e., the number of retweets a message receives) and structural virality (i.e., quantified distinction between broadcast and viral diffusion) of information diffusion on Twitter. The study collected 425 unique tweets posted by CDC during a 17-week period and constructed a diffusion tree for each unique tweet. Findings indicated that, with respect to message features, perceived efficacy after reading a tweet positively predicted diffusion size of the tweet, whereas perceived susceptibility to a health condition after reading a tweet positively predicted structural virality of the tweet. Perceived negative emotion positively predicted both size and structural virality. With respect to network features, the level of involvement of brokers in diffusing a tweet increased the tweet's structural virality. Theoretical and practical implications were discussed on disseminating health information via broadcasting and viral diffusion on social media. |
format | Online Article Text |
id | pubmed-7127591 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71275912020-04-08 Diffusion size and structural virality: The effects of message and network features on spreading health information on twitter Meng, Jingbo Peng, Wei Tan, Pang-Ning Liu, Wuyu Cheng, Ying Bae, Arram Comput Human Behav Article Relying on diffusion of innovation theory, this study examines the impacts of perceived message features and network characteristics on size (i.e., the number of retweets a message receives) and structural virality (i.e., quantified distinction between broadcast and viral diffusion) of information diffusion on Twitter. The study collected 425 unique tweets posted by CDC during a 17-week period and constructed a diffusion tree for each unique tweet. Findings indicated that, with respect to message features, perceived efficacy after reading a tweet positively predicted diffusion size of the tweet, whereas perceived susceptibility to a health condition after reading a tweet positively predicted structural virality of the tweet. Perceived negative emotion positively predicted both size and structural virality. With respect to network features, the level of involvement of brokers in diffusing a tweet increased the tweet's structural virality. Theoretical and practical implications were discussed on disseminating health information via broadcasting and viral diffusion on social media. Elsevier Ltd. 2018-12 2018-07-28 /pmc/articles/PMC7127591/ /pubmed/32288177 http://dx.doi.org/10.1016/j.chb.2018.07.039 Text en © 2018 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Meng, Jingbo Peng, Wei Tan, Pang-Ning Liu, Wuyu Cheng, Ying Bae, Arram Diffusion size and structural virality: The effects of message and network features on spreading health information on twitter |
title | Diffusion size and structural virality: The effects of message and network features on spreading health information on twitter |
title_full | Diffusion size and structural virality: The effects of message and network features on spreading health information on twitter |
title_fullStr | Diffusion size and structural virality: The effects of message and network features on spreading health information on twitter |
title_full_unstemmed | Diffusion size and structural virality: The effects of message and network features on spreading health information on twitter |
title_short | Diffusion size and structural virality: The effects of message and network features on spreading health information on twitter |
title_sort | diffusion size and structural virality: the effects of message and network features on spreading health information on twitter |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7127591/ https://www.ncbi.nlm.nih.gov/pubmed/32288177 http://dx.doi.org/10.1016/j.chb.2018.07.039 |
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