JOURNAL ARTICLE

Spread Dynamics of Tourism-Related Messages within Social Networks.

  • Published In: Journal of Information & Knowledge Management, 2023, v. 22, n. 2. P. 1 1 of 3

  • Database: The Belt and Road Initiative Reference Source 2 of 3

  • Authored By: Luo, Dan; Xiong, Bojian; Cao, Yu 3 of 3

Abstract

Tourism-related messages can alter the images of tourism destinations. In the new media time, messages from individual perception of the destination can spread among the social networks. Here, based on three basic assumptions, we developed a model to investigate the spread dynamics of tourism-related messages. In the model, two variables of individual behaviour, representing the probabilities of sharing or forgetting the messages, respectively, and a variable to represent the message's importance were integrated. Within the simulated small-world networks, we observed two distinct patterns in the spread dynamics. The patterns were determined by individuals' willingness to share messages and the message's importance. If a majority of people choose not to send a message that they have received, the informed population will eventually become negligible; whereas, while they are inclined to spread, the informed population will remain constant over time. These patterns were influenced by neither the density of network connections nor the message sources. The message sources only determine the speed and the scale of diffusion. In summary, our model revealed the patterns of the spread of tourism-related messages. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Information & Knowledge Management. 2023/04, Vol. 22, Issue 2, p1
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2023
  • ISSN:0219-6492
  • DOI:10.1142/S0219649222500964
  • Accession Number:163408755
  • Copyright Statement:Copyright of Journal of Information & Knowledge Management is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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