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.

地理标记，共享和自动分析来自社交网站（SNS）的在线内容的最新进展为分析人与自然的互动提供了前所未有的机会。但是，该领域的先前研究对于自动内容分析的好处（尤其是大规模的），由于SNS来源的选择而引起的偏差以及基于SNS数据的访问模型的预测能力，提供的见解有限。我们在以色列和巴勒斯坦权力机构的568个保护区中探索与现场经验相关的强度，兴趣和情感方面的定量和定性方面。我们分析了来自四个不同SNS的超过100,000张照片和推文的计数和内容，校准了访问模型并预测了在不受监控的网站中的访问，基于在线照片中反映的人与自然互动的类型，对网站进行聚类，并表征与单个网站及其聚类中的体验相关的情感极性。我们发现，将多个来源的数据进行合并，并控制与站点的上镜性和人与自然互动类型有关的偏见，会带来好处。我们的结果表明，当前对不受监视的站点的访问量的最佳估计低估了实际访问量的39％。我们讨论了这项研究中的技术和发现如何适用于更广泛的环境和文化景点的管理和保护。我们发现，将多个来源的数据进行合并并控制与站点的上镜性和人与自然互动类型有关的偏见有很多好处。我们的结果表明，当前对不受监视的站点的访问量的最佳估计低估了实际访问量的39％。我们讨论了这项研究中的技术和发现如何适用于更广泛的环境和文化景点的管理和保护。我们发现，将多个来源的数据进行合并，并控制与站点的上镜性和人与自然互动类型有关的偏见，会带来好处。我们的结果表明，当前对不受监视的站点的访问量的最佳估计低估了实际访问量的39％。我们讨论了这项研究中的技术和发现如何适用于更广泛的环境和文化景点的管理和保护。