Destination eWOM: A macro and meso network approach?
研究了目的地举办标志性活动期间，Twitter上公开分享的电子口碑特征及驱动因素，通过5年数据的社会网络分析发现少数账号主导信息传播，且内容随活动变化。
The purpose of this paper is to develop a framework that describes the characteristics and the underlying drivers of publically shared electronic word-of-mouth (eWOM) for destinations. Tweets about a destination were collected while the destination hosted a hallmark event over a 5-year period (2011–2015). In each year, interactions on Twitter were analysed using macro and meso-level social network analysis to identify the network structure and hubs of eWOM activity. A K means clustering algorithm was then applied to create clusters of nodes with similar characteristics and eWOM content within each cluster was analysed using automated content analysis. The resulting model indicates that destination and event eWOM maintains a macro network structure in which a small number of accounts or hubs influence information sharing. Hub characteristics evolve over time, whereas eWOM content can fluctuate in response to emergent destination activities.