RQ1: By analyzing the social network, can popular posts be identified, and can they recognize solutions to promote a destination?
RQ2: How does the social network of a tourist location develop over time?
3. Literature Review
3.1. Destination Tourism
3.2. Collaboration, Knowledge, and Information-Sharing
RQ1: By analyzing the social network, can popular posts be identified, and can they recognize solutions to promote a destination? The posting in reference to the restaurant Slippen certainly brought a level of pride to the followers on Facebook and it was found to be the number one conversation. The comments were extremely positive and those who had already visited the restaurant provided their recommendations by sharing positive experiences, which resulted in those who had not yet visited the restaurant to comment that they wanted to eat at the Slippen restaurant. There were many people who decided to share their positive experiences at the Slippen restaurant, resulting in the following: page rank, 20,578; positive likes, 944; positive comments, 58; and positive shares, 425. As previously illustrated in Figure 3 at the top-left part of the diagram, this post had the highest eigenvector centrality value, which was also similarly demonstrated in Table 2.
RQ2: How does the social network of a tourist location develop over time? The online network has grown denser over time as the tourism authority has steadily been able to increase its online presence and more people have chosen to follow them over time. The modularity value of 0.87 also indicates that there are dense or deep connections between nodes in the groups but only sparse or thin connections or edges between the nodes in other groups. It is paramount to study the development of community structure in a social network; a cohesive group of nodes that are connected to each other are denser than that of the nodes in other communities. Such networks further provide insight into how the network structure and topology interact. Although it is difficult to assess the community and nature of the group, various approaches have been developed and used with varying degrees of success. Modularity is one such concept that provides information on how the communities within social networks are formed. Modularity is the fraction of the edges that fall within the given network and the approximate fraction is less if the edges are distributed randomly. In addition, most businesses in Fredrikstad have their own Facebook site, thus promoting more exposure to the old part of the town while providing separate marketing streams for the destination.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Single vertex connected components||121|
|Max. vertices in a connected component||53|
|Max. edges in a connected component||62|
|Max. geodesic distance (diameter)||4|
|Average geodesic distance||1.787445|
|Posting||In-Degree||Out-Degree||Betweenness Centrality||Closeness Centrality||Eigenvector|
|Posting||Page Rank||Clustering |
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