Outdoor and nature-based recreation provides countless social benefits, yet public land managers often lack information on the spatial and temporal extent of recreation activities. Social media is a promising source of data to fill information gaps because the amount of recreational use is positively correlated with social media activity. However, despite the implication that these correlations could be employed to accurately estimate visitation, there are no known transferable models parameterized for use with multiple social media data sources. This study tackles these issues by examining the relative value of multiple sources of social media in models that estimate visitation at unmonitored sites and times across multiple destinations. Using a novel dataset of over 30,000 social media posts and 286,000 observed visits from two regions in the United States, we compare multiple competing statistical models for estimating visitation. We find social media data substantially improve visitor estimates at unmonitored sites, even when a model is parameterized with data from another region. Visitation estimates are further improved when models are parameterized with on-site counts. These findings indicate that while social media do not fully substitute for on-site data, they are a powerful component of recreation research and visitor management.

户外和基于自然的娱乐提供了无数的社会效益，但公共土地管理者往往缺乏有关娱乐活动的空间和时间范围的信息。社交媒体是填补信息空白的有希望的数据来源，因为娱乐使用量与社交媒体活动呈正相关。然而，尽管暗示可以使用这些相关性来准确估计访问量，但没有已知的可转移模型参数化以用于多个社交媒体数据源。本研究通过在模型中检验多个社交媒体来源的相对价值来解决这些问题，这些模型估计了多个目的地的未受监控站点的访问量和时间。使用包含 30,000 多个社交媒体帖子和 286 个新数据集，000 观察来自美国两个地区的访问，我们比较了多个竞争统计模型来估计访问。我们发现社交媒体数据显着改善了未受监控站点的访问者估计，即使模型是使用来自另一个区域的数据进行参数化的。当使用现场计数对模型进行参数化时，访问估计会得到进一步改善。这些发现表明，虽然社交媒体不能完全替代现场数据，但它们是娱乐研究和游客管理的强大组成部分。当使用现场计数对模型进行参数化时，访问估计会得到进一步改善。这些发现表明，虽然社交媒体不能完全替代现场数据，但它们是娱乐研究和游客管理的强大组成部分。当使用现场计数对模型进行参数化时，访问估计会得到进一步改善。这些发现表明，虽然社交媒体不能完全替代现场数据，但它们是娱乐研究和游客管理的强大组成部分。