Digital Culture & AudioVisual Challenges

In the modern era, a major factor in the dissemination of cultural events, including audiovisual art projects of any type, is their presence in the World Wide Web [1]. Web media outlets, cultural websites, blogs, social media and other online repositories provide ample information concerning art events. But news information on the Web is often both fragmented and ephemeral, due to factors such as the difference in interests between legacy media and user generated media coverage [2,3] and the popularity of algorithm-driven news distribution channels [4]. In order to gain more valid input on the characteristics of contemporary audiovisual art projects, extracting information from multiple sources is paramount. Moreover, the process of identifying and collecting information about a singular event from different websites can lead to an informed estimation of its potential outreach, which in turn may be a factor in determining its cultural impact [5].
This study focuses on researching the feasibility of implementing a detailed methodology that takes advantage of modern Web data extraction techniques as well as the capabilities of generative artificial intelligence to summarize and combine information from multiple online sources, in order to collect data regarding contemporary audiovisual art events. As a means of investigating this feasibility a comprehensive Web data extraction algorithm was designed, implemented and tested in a pilot data collection process that spanned several days and gathered information regarding events that took place in Greece.
The proposed algorithm takes advantage of machine readability features found in the modern Web, namely the integration of semantic Web technologies and social media data graphs, which are playing an ever more important role in the dissemination of art and culture related content [6]. Moreover, the algorithm uses the power of generative AI to perform content analysis on textual descriptions provided in website articles, a process which has been a key feature of generative AI for many decades [7]. Though this approach, general information derived from a vast variety of different websites can be converted into structured data, which may be used as a robust basis for further analysis of the classification and outreach of audio visual art projects.
The study emphasizes the technical challenges of this implementation, as well as the experience gained, both through the process of development and through the achieved results. Moreover, it presents a summary of the collected information and discusses its quality and potential for analysis.

[1] Leibovitz Libedinsky, T., Roig Telo, A., & Sánchez-Navarro, J. (2015). Up close and personal: exploring the bonds between promoters and backers in audiovisual crowdfunded projects.
[2] Maier, S. (2010). All the news fit to post? Comparing news content on the web to newspapers, television, and radio. Journalism & Mass Communication Quarterly, 87(3-4), 548-562.
[3] Boczkowski, P. J., & Mitchelstein, E. (2013). The news gap: When the information preferences of the media and the public diverge. MIT press.
[4] Martens, B., Aguiar, L., Gomez-Herrera, E., & Mueller-Langer, F. (2018). The digital transformation of news media and the rise of disinformation and fake news.
[5] Nadotti, L., & Vannoni, V. (2019). Cultural and event tourism: an interpretative key for impact assessment. Eastern journal of European studies, 10(1), 115.
[6] Giannakoulopoulos, A., Pergantis, M., Konstantinou, N., Kouretsis, A., Lamprogeorgos, A., & Varlamis, I. (2022). Estimation on the importance of semantic web integration for art and culture related online media outlets. Future Internet, 14(2), 36.
[7] Maybury, M. T. (1995). Generating summaries from event data. Information Processing & Management, 31(5), 735-751.