Identifying Museum Visitors via Social Network Analysis of Instagram

Abstract

As social networking services (SNSs) have become increasingly influential, they are now a vital element in art museums because communication with visitors is crucial. However, conventional methods of visitor studies do not consider the characteristics of SNSs. Even when a museum uses a SNS as a marketing tool, it cannot sufficiently analyze the data to understand the visitor. Additionally, linking the SNS analysis content with the actual visitor in the museum requires an application to identify visitor types. Therefore, we extracted communities through a social network analysis of museum followers, analyzed the characteristics for specific groups, and designed a web-based, visitor-type analysis application for art museums based on text-similarity measurements. The experimental results demonstrated that followers of each art museum on a SNS form a community with similar characteristics or interests. Furthermore, our developed application provides a new way to analyze the type of visitors in an art museum. Consequently, because it can automatically identify visitor types, it improves the exhibition experience of visitors by linking them with the exhibition contents or guide services and is a new attempt to connect a SNS with the museum visitor.