Using Travelpod.com, this paper aims to provide a methodology to locate central groups of travelers and to describe pattern characteristics of central travelers.
The paper uses snowball sampling to locate travelers and analyze their hyperlink interconnections to identify central travelers' groups. Analysis of the adjacency matrix of the social network of travelers using multidimensional scaling and hierarchical cluster analysis to identify core travelers' groups follows.
In total, 7 percent of travelers are considered central travelers. They form core groups containing the most active and information providing travelers. Group membership is correlated with common travelers' characteristics.
The research is limited to a specific network of travelers, to a specific time interval, and to a specific sampling method. Repetition of the study in other travelers' networks in several time instances using a full list of member travelers would help to generalize the findings. Also, graph theoretical approaches other than the statistical analysis used could reveal more properties.
Travelers in core groups are more likely to be reached by others who navigate through a series of incoming links that lead to them and it is probable that these travelers have the potential to address many visitors and therefore to have a significant impact on the provision of information.
The originality of the paper lies in the use of multivariate statistics on the network adjacency matrix to locate core travelers groups and on finding groups of the most influential travelers.
