Using graph theory and social media data to assess cultural ecosystem services in coastal areas: Method development and application

TL;DR

This study develops a graph theory-based method to assess cultural ecosystem services using social media data, demonstrating its effectiveness and advantages over photo content analysis across multiple platforms and global case studies.

Contribution

Introduces a novel graph theory network analysis approach for CES assessment using social media hashtags, offering improved insights and reduced bias compared to traditional photo analysis.

Findings

GTNA provides similar CES assessment capabilities as photo analysis.

GTNA better identifies relational and eudaimonic values linked to nature.

Method is cost-effective, easy to apply, and suitable for large-scale studies.

Abstract

The use of social media (SM) data has emerged as a promising tool for the assessment of cultural ecosystem services (CES). Most studies have focused on the use of single SM platforms and on the analysis of photo content to assess the demand for CES. Here, we introduce a novel methodology for the assessment of CES using SM data through the application of graph theory network analyses (GTNA) on hashtags associated to SM posts and compare it to photo content analysis. We applied the proposed methodology on two SM platforms, Instagram and Twitter, on three worldwide known case study areas, namely Great Barrier Reef, Galapagos Islands and Easter Island. Our results indicate that the analysis of hashtags through graph theory offers similar capabilities to photo content analysis in the assessment of CES provision and the identification of CES providers. More importantly, GTNA provides greater capabilities at identifying relational values and eudaimonic aspects associated to nature, elusive aspects for photo content analysis. In addition, GTNA contributes to the reduction of the interpreter's bias associated to photo content analyses, since GTNA is based on the tags provided by the users themselves. The study also highlights the importance of considering data from different social media platforms, as the type of users and the information offered by these platforms can show different CES attributes. The ease of application and short computing processing times involved in the application of GTNA makes it a cost-effective method with the potential of being applied to large geographical scales.