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Affected by urbanization, centralization and the decrease of overallpopulation, Japan has been making efforts to revitalize the rural areas acrossthe country. One particular effort is to increase tourism to these rural areasvia regional branding, using local farm products as tourist attractions acrossJapan. Particularly, a program subsidized by the government called Michinoeki,which stands for 'roadside station', was created 20 years ago and it strives toprovide a safe and comfortable space for cultural interaction between roadtravelers and the local community, as well as offering refreshment, andrelevant information to travelers. However, despite its importance in therevitalization of the Japanese economy, studies with newer technologies andmethodologies are lacking. Using sales data from establishments in the Kyushuarea of Japan, we used Support Vector to classify content from Twitter intorelevant topics and studied their causal relationship to the sales for eachestablishment using LiNGAM, a linear non-gaussian acyclic model built forcausal structure analysis, to perform an improved market analysis consideringmore than just correlation. Under the hypotheses stated by the LiNGAM model, wediscovered a positive causal relationship between the number of tweetsmentioning those establishments, specially mentioning deserts, a need forbetter access and traf^ic options, and a potentially untapped customer base inmotorcycle biker groups.

Causal relationship between eWOM topics and profit of rural tourism at Japanese Roadside Stations "MICHINOEKI"