By Iis Tussyadiah, Alessandro Inversini
The papers offered during this quantity improve the state of the art learn on colossal info and analytics, social media, digital advertising and marketing, cellular computing and recommender structures, cellular sensors and geosocial companies, augmented truth, wearable computing, shrewdpermanent tourism, digital distribution for tourism and hospitality services and products, e-learning, responsive website design and administration, and eTourism for improvement. This ebook covers the main major parts contributed through trendy students from around the globe and is acceptable for either lecturers and practitioners who're drawn to the newest advancements in e-Tourism.
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Additional info for Information and Communication Technologies in Tourism 2015: Proceedings of the International Conference in Lugano, Switzerland, February 3 - 6, 2015
2014). In Leavitt et al. (2009) and Myers and Leskovec (2014), authors show that level of users’ activity (number of tweets) depends upon retweets and their in-degree centrality (number of followers). In social media, while sharing content, users may be referred as ‘generalist’ or 24 C. Francalanci and A. Hussain ‘information sources’ who talk about multiple topics (Hutto et al. 2013). On the contrary, there exist users who are very speciﬁc in sharing content related to speciﬁc topics or brands.
2003). Visualisation of power-law network topologies. Paper presented at the Networks, 2003. ICON2003. The 11th IEEE International Conference on (pp. 69–74). IEEE. Chen, C. (2006). Information visualization: Beyond the horizon. New York, NY: Springer. , & Gordon, M. D. (2014). The power of social media analytics. Communications of the ACM, 57(6), 74–81. , & Hussain, A. (2014). A visual approach to the empirical analysis of social inﬂuence. Paper presented at the DATA 2014—Proceedings of 3rd International Conference on Data Management Technologies and Applications.
001. Hypothesis H1 Hypothesis H1 has been tested through correlation. 01 level of signiﬁcance, supporting the hypothesis H1. Authors by having greater probability of sharing contents, can be potential inﬂuencers in network. Similarly, through visualization results, Fig. e. e. to talk about multiple topics). Hypothesis H2 Similarly hypothesis H2, has been tested through correlation. 01 level of signiﬁcance, supporting the hypothesis H2. It means that, authors, who have large number of retweets, can also be ‘information sources’ or ‘generators’.