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        검색결과 1

        1.
        2016.07 구독 인증기관·개인회원 무료
        Substantial knowledge about how electronic word of mouth (eWOM) relates to influentials, or, individuals who either intentionally or unintentionally have a great impact on the network, is lacking because influence patterns are hardly quantifiable when human and societal components are not deeply understood. The eWOM networks in social media platforms drive the theoretical and practical progress of the social structure of eWOM and the social role of influentials. The purpose of this study is to explore the diffusion of eWOM in a Twitter network by drawing from the Diffusion of Innovation theory in conjunction with Social Network Analysis (SNA) approach. Through a large amount of data collected from Twitter regarding the terrorist attack in Paris on November 13, 2015, this study examined the social structure of eWOM network groups and identifies the social role of influential users in the network of #prayforparis. To identify influentials of eWOM in Twitter networks, SNA was conducted with NodeXL, which is an Excel add-in network analysis software. The Twitter data set was uploaded at 11 am, on November 17, 2015 by applying hashtag prayforparis through Twitter Search Network feature of NodeXL. A total of 19,592 tweets (vertices) were downloaded with a total of 20,297 edges (relations between vertices). “Justin Bieber” was identified as the most influential and dominant user in the network of #prayforparis because he has the highest in-degree (4,196) and highest betweenness centrality (51,772,745.861). Justin Bieber lost one of his friends in the terrorist attack, and his tweet about the sadness from the loss was retweeted more than 50,000 times and received 77,000 likes. Throughout the network, top word pairs respectively repeated more than 4,000 times were “thomas, prayforparis,” “rt, justinebieber,” “rip, thomas,” and “justinbiber, rip.” “Ally Brook” and “Louis Tomlin” ranked 3rd and 5th respectively with the highest betweenness centrality also demonstrated the celebrity power in the eWOM network of #prayforparis. While the three celebrities as dominant influentials were prominent in the Twitter network of #prayforparis, the social roles of fans were also acknowledged. The celebrities’ fans and fan sites have constantly diffused the celebrities’ posts by retweeting and by mentioning every tweet posted by the celebrities that they support. Without the celebrities and their fans, the #prayforparis network would have depicted a completely different style of diffusion of eWOM. The study confirmed social role of fans that constantly presented idiosyncratic manners by identifying and by linking to the public figure. The distinctive characteristics of the fans also enabled identifying the social role of the celebrity while spreading the new ideas with hashtag. The celebrity played a key role as an information hub even in a network of socio-political events such as the #prayforparis Twitter network. Thus, identified overwhelming influence of the celebrities in the eWOM network extends the areas of marketing choices and applicable strategies with celebrities.