Diffusion size and structural virality: The effects of message and network features on spreading health information on twitter

Jingbo Meng, Wei Peng, Pang Ning Tan, Wuyu Liu, Ying Cheng, Arram Bae

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)111-120
Number of pages10
JournalComputers in Human Behavior
Volume89
DOIs
StatePublished - Dec 2018
Externally publishedYes

Keywords

  • Health information
  • Information diffusion
  • Social media
  • Social network
  • Structural virality

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • Psychology(all)

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