Audiovisual Art Event Classification and Outreach Based on Web Extracted Data

Abstract

The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information may become a robust source of real-world data, which may form the basis of an objective data-driven analysis. In this study, a methodology for collecting information about audio and visual art events in an automated manner from a large array of websites is presented in detail. This process uses cutting edge Semantic Web, Web Search and Generative AI technologies to convert website documents into a collection of structured data. The value of the methodology is demonstrated by creating a large dataset concerning audiovisual events in Greece. The collected information includes event characteristics, estimated metrics based on their text descriptions, outreach metrics based on the media that reported them, and a multi-layered classification of these events based on their type, subjects and methods used. This dataset is openly provided to the general and academic public through a Web application. Moreover, each event’s outreach is evaluated using these quantitative metrics, the results are analyzed with an emphasis on classification popularity and useful conclusions are drawn concerning the importance of artistic subjects, methods, and media.