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.

来自 Flickr 等社交媒体网站的图像和文本元数据已被用于了解文化生态系统服务 (CES) 的驱动因素和分布。但是，使用来自社交媒体网站的所有可用数据可能无法准确表示单个服务。例如，自然图像可能在图像的描述中被负面描述。在这里，我们提出了一种改进社交媒体数据以更好地表示 CES 的新方法，包括按关键字过滤、照片内容以及通过包含文本元数据中表达的情绪度量来丰富数据。我们证明，与完整数据集相比，代表美国徒步旅行的丰富 Flickr 图像数据集的分布可以产生不同的结果和结论。此外，我们对这些徒步旅行图像的内容进行了分类，并使用潜在语义分析，根据其内容的相似性将这些图像分为十组。这些团体提供了丰富的信息，例如地理多样性和生物多样性在支持积极的徒步旅行体验方面的重要性。这种方法的应用有助于丰富 CES 研究的社交媒体数据，使研究人员能够进一步理清推动 CES 分布、收益和价值的复杂社会生态相互作用。