Abstract
We contribute a system design and a generalized formal methodology to segment tourists based on their geolocated blogging behaviour according to their interests in identified tourist hotspots. Thus, it is possible to identify and target groups that are possibly interested in alternative destinations to relieve overtourism. A pilot application in a case study of Chinese travel in Switzerland by analysing Qyer travel blog data demonstrates the potential of our method. Accordingly, we contribute four conclusions supported by empirical data. First, our method can enable discovery of plausible geographical distributions of tourist hotspots, which validates the plausibility of the data and its collection. Second, our method discovered statistically significant stochastic dependencies that meaningfully differentiate the observed user base, which demonstrates its value for segmentation. Furthermore, the case study contributes two practical insights for tourism management. Third, Chinese independent travellers, which are the main target group of Qyer, are mainly interested in the discovered travel hotspots, similar to tourists on packaged tours, but also show interest in alternative places. Fourth, the proposed user segmentation revealed two clusters based on users’ social media activity level. For tourism research, users within the second cluster are of interest, which are defined by two segmentation attributes: they blogged about more than just one location, and they have followers. These tourists are significantly more likely to be interested in alternative destinations out of the hotspot axis. Knowing this can help define a target group for marketing activities to promote alternative destinations.
Highlights
With the development of social media over the last two decades, unstructured data have become increasingly relevant for scientific research
We segmented the users according to their social media activity to gain insights on what differentiates users with interests in less frequently commented on places, with the aim of using these insights to define a target group for managing overtourism
We contribute a system design for detecting tourism hotspots and for segmenting tourists based on their interests in these hotspots, in the case of the geolocated social medium Qyer.com
Summary
With the development of social media over the last two decades, unstructured data have become increasingly relevant for scientific research. Data on opinions and interests are publicly available in the form of user-generated content (UGC) These data can be collected and consolidated, information can be extracted for tourism research, and data analysis such as geographical distribution or user segmentation can be performed. Interesting dimensions of social media analysis are time (trends), region (geodata), users and user groups (influencers), moods (sentiment analysis), and social structures (network analysis) Analysing these dimensions enhances the understanding of the social media context of tourists and tourism destinations. Being a relatively new field, SMM for computational social science raises methodological questions that need to be answered to strengthen its acceptance. It is unclear how unstructured and semi-structured data can be consolidated to derive conceptual statements that support answering scientific questions. New methods and systems need to be developed and evaluated to generate knowledge from social media data
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