Recent advances in geotagging, sharing and automatically analyzing online content from Social Networking Sites (SNS) offer unprecedented opportunities for the analysis of human-nature interactions. Previous studies in this field, however, offer limited insights regarding the benefits of automated content analysis especially at large scales, biases arising from the selection of SNS sources, and the predictive power of visitation models based on SNS data. We explore quantitative and qualitative aspects related to intensity, interests and sentiments associated with on-site experiences in 568 protected areas in Israel and the Palestinian Authority. We analyze counts and content of >100,000 photographs and tweets from four different SNSs, calibrate visitation models and predict visitation in unmonitored sites, cluster sites based on the typology of human-nature interactions reflected in online photographs, and characterize the polarity of sentiments associated with experiences in individual sites and clusters thereof. We find benefits in combining data from multiple sources and controlling for biases related to sites’ photogenicity and type of human-nature interactions. Our results suggest that current best estimates of visitation in unmonitored sites underestimate by 39% the actual number of visits. We discuss how the techniques and findings in this study are applicable in the broader context of the management and conservation of sites of environmental or cultural interest.
Bibliographical noteFunding Information:
This research (Grant No. 2751/16) was supported by the Israel Science Foundation within the ISF-UGC joint research program framework. We are grateful to Amir Shmulik and Alon Lotan for their assistance and input at various stages of the present research.
© 2020 Elsevier Ltd
- Cultural ecosystem services
- Heritage tourism
- Nature-based recreation
- Protected areas
- Social media
ASJC Scopus subject areas
- Global and Planetary Change
- Geography, Planning and Development
- Management, Monitoring, Policy and Law