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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| Vydáno v: | Computers in human behavior Ročník 89; s. 111 - 120 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Elsevier Ltd
01.12.2018
Elsevier Science Ltd |
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| ISSN: | 0747-5632, 1873-7692, 0747-5632 |
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| Abstract | 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.
•Tweets that increased perceived efficacy had more retweets.•Tweets that increased perceived susceptibility had higher structural virality.•Perceived negative emotion predicted both diffusion size and structural virality.•Broker involvement in diffusing a tweet increased its structural virality. |
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| AbstractList | 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.
•
Tweets that increased perceived efficacy had more retweets.
•
Tweets that increased perceived susceptibility had higher structural virality.
•
Perceived negative emotion predicted both diffusion size and structural virality.
•
Broker involvement in diffusing a tweet increased its structural virality. 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.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. 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. •Tweets that increased perceived efficacy had more retweets.•Tweets that increased perceived susceptibility had higher structural virality.•Perceived negative emotion predicted both diffusion size and structural virality.•Broker involvement in diffusing a tweet increased its structural virality. 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. |
| Author | Tan, Pang-Ning Liu, Wuyu Bae, Arram Cheng, Ying Peng, Wei Meng, Jingbo |
| AuthorAffiliation | c Department of Computer Science and Engineering, Michigan State University, East Lansing, USA b Department of Media and Information, Michigan State University, East Lansing, USA a Department of Communication, Michigan State University, East Lansing, USA d Department of Communication, Bellarmine University, Louisville, KY, USA |
| AuthorAffiliation_xml | – name: a Department of Communication, Michigan State University, East Lansing, USA – name: c Department of Computer Science and Engineering, Michigan State University, East Lansing, USA – name: d Department of Communication, Bellarmine University, Louisville, KY, USA – name: b Department of Media and Information, Michigan State University, East Lansing, USA |
| Author_xml | – sequence: 1 givenname: Jingbo surname: Meng fullname: Meng, Jingbo email: jingbome@msu.edu organization: Department of Communication, Michigan State University, East Lansing, USA – sequence: 2 givenname: Wei surname: Peng fullname: Peng, Wei organization: Department of Media and Information, Michigan State University, East Lansing, USA – sequence: 3 givenname: Pang-Ning surname: Tan fullname: Tan, Pang-Ning organization: Department of Computer Science and Engineering, Michigan State University, East Lansing, USA – sequence: 4 givenname: Wuyu surname: Liu fullname: Liu, Wuyu organization: Department of Communication, Bellarmine University, Louisville, KY, USA – sequence: 5 givenname: Ying surname: Cheng fullname: Cheng, Ying organization: Department of Communication, Michigan State University, East Lansing, USA – sequence: 6 givenname: Arram surname: Bae fullname: Bae, Arram organization: Department of Communication, Michigan State University, East Lansing, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32288177$$D View this record in MEDLINE/PubMed |
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| Keywords | Social network Information diffusion Structural virality Social media Health information |
| Language | English |
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| Title | Diffusion size and structural virality: The effects of message and network features on spreading health information on twitter |
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