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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...

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Detalles Bibliográficos
Autores principales: Meng, Jingbo, Peng, Wei, Tan, Pang-Ning, Liu, Wuyu, Cheng, Ying, Bae, Arram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2018
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.
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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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