Understanding the sentiment associated with cultural ecosystem services using images and text from social media

Ilan Havinga*, Diego Marcos, Patrick Bogaart, Devis Tuia, Lars Hein

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Social media is increasingly being employed to develop Cultural Ecosystem Services (CES) indicators. The image-sharing platform Flickr has been one of the most popular sources of data. Most large-scale studies, however, tend to only use the number of images as a proxy for CES due to the challenges associated with processing large amounts of this data but this does not fully represent the benefit generated by ecosystems in terms of the positive experiences expressed by users in the associated text. To address this gap, we apply several Computer Vision (CV) and natural language processing (NLP) models to link CES estimates for Great Britain based on the content of images to sentiment measures using the accompanying text, and compare our results to a national, geo-referenced survey of recreational well-being in England. We find that the aesthetic quality of the landscape and the presence of particular wildlife results in more positive sentiment. However, we also find that different physical settings correlate with this sentiment and that sentiment is sometimes more strongly related to social activities than many natural factors. Still, we find significant associations between these CES measures, sentiment and survey data. Our findings illustrate that integrating sentiment analysis with CES measurement can capture some of the positive benefits associated with CES using social media. The additional detail provided by these novel techniques can help to develop more meaningful CES indicators for recreational land use management.

Original languageEnglish
Article number101581
JournalEcosystem Services
Volume65
DOIs
Publication statusPublished - Feb 2024

Keywords

  • Big data
  • Cultural ecosystem services
  • Ecosystem services
  • Machine learning
  • Natural language processing
  • Social media

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