Outbound behavior analysis through social network data: A case study of Chinese people in Japan

摘要

Globalization leads to more and more outbound behavior. Understanding these behavior is of great significance to both outbound and inbound countries in a lot of fields. However, the previous research on outbound behavior mainly unitizes questionnaires or purely analyzes the statistical data, these data are either too difficult to acquire or unable to directly analyze public opinion. This paper aim to analyze outbound behavior through large volume of text and location information published on social network services (SNS) and look into a case study of Chinese people in Japan with the data acquired from Chinese microblog website, Sina Weibo. For text analysis, we utilize an adapted latent Dirichlet allocation model to extract the topics in text and discuss the spatial and temporal distribution of the topics. The experiment proves that the topics represent outbound behavior of tourists and foreign visitors can be found and explained in the experiment dataset, which indicates that SNS data is valid for analyzing outbound behavior.