Enriching social media data allows a more robust representation of cultural ecosystem services

17317 10 94733 3 275716 document 94733 enriching_preprint.pdf application/pdf 70f2ab857b2ce40c2ebe5f84dd8eefc4 MD5 1839522 2021-07-19 12:25:45 https://pure.iiasa.ac.at/id/eprint/17317/1/enriching_preprint.pdf 17317 1 1 application/pdf text en public cc_by_nc_nd_4

enriching_preprint.pdf

2023-07-13 accepted 94734 2 275719 document 94734 lightbox.jpg image/png a25c6f7b52a1a24287a8f87afee25da1 MD5 55275 2021-07-19 12:36:00 https://pure.iiasa.ac.at/id/eprint/17317/2/lightbox.jpg 17317 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en staffonly

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/94733 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/94733 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/94733 archive 353 disk0/00/01/73/17 2021-07-12 07:23:55 2023-07-13 03:00:03 2021-07-12 07:23:55 article show Fox N. Graham L. 9309 Eigenbrod F. Bullock J.M. Parks K.E. Enriching social media data allows a more robust representation of cultural ecosystem services pub BNR BEC Images and textual metadata from social media sites such as Flickr have been used to understand the drivers and distributions of cultural ecosystem services (CES). However, using all available data from social media sites may not provide an accurate representation of individual services. For example, an image of nature might be described negatively in the image’s description. Here, we present a novel approach to refining social media data to represent CES better, including filtering by keywords, photograph content and enriching the data by including a measure of the sentiment expressed in the textual metadata. We demonstrate that the distribution of an enriched dataset of Flickr images representing hiking in the USA can contribute to different results and conclusions than the full dataset. Furthermore, we classified the contents of these hiking images and, using latent semantic analysis, clustered the images into ten groups based on the similarity of their content. The groups provide rich information, such as the importance of geodiversity and biodiversity in supporting a positive hiking experience. The application of this method can help to enrich social media data for CES studies, allowing researchers to further untangle the complex socio-ecological interactions that drive CES distributions, benefits and values. 2021-08 published 10.1016/j.ecoser.2021.101328 3168 public Ecosystem Services 50 e101328 TRUE 2212-0416 FALSE no info:eu-repo/semantics/article