Destination marketing organizations (DMOs) use Twitter to promote attractions and special events and to build brand awareness. Tweets of a DMO spread through a complex network of connected accounts. Some of these are more influential than others due to their position within the network. This paper aims to use a network analysis of 14 DMOs to identify the categories of influencers that have the greatest reach.
NodeXL was used to download and analyze network data from Twitter during July 2016 for a collection of DMOs promoting US cities. Accounts in the networks were ranked using several measures of relative influence such as the number of times the accounts mentioned/retweeted others or were mentioned in posts about the DMO. The most influential accounts in the network were identified and coded by category.
Media, promotional accounts and those of individuals were determined to be influential by each metric considered. Stakeholders such as hotels and restaurants occupy positions of low importance in the networks and generally do not capitalize on opportunities provided by the DMOs.
DMOs can seek out strategic partnerships with key influencers to maximize their effectiveness. Additionally, stakeholders can improve their Twitter presence by interacting with the DMOs and other influential accounts.
This paper identifies influencers that can aid in DMOs’ marketing campaigns. It also presents a methodology that can monitor the effectiveness of such campaigns, something absent in the current literature.
The authors would like to thank Toni Hilsin, Christine Schairer, Justine Ciraolo and Peter Gallagher of Stockton University for assistance with the procurement and configuration of the software used in this study. This work also benefitted greatly from discussions with Marc Smith of the Social Media Research Foundation.
Bokunewicz, J.F. and Shulman, J. (2017), "Influencer identification in Twitter networks of destination marketing organizations", Journal of Hospitality and Tourism Technology, Vol. 8 No. 2, pp. 205-219. https://doi.org/10.1108/JHTT-09-2016-0057Download as .RIS
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