Availability of User Generated Content and the development of Big Data and machine learning algorithms have paved the way to collecting and analysing great volumes of data. We scan imagery data from traveling-related posts on Instagram to identify the key features of the destination image and of its dynamics. Specifically, we exploit a newly introduced Visual Object Recognition tool (Google Cloud Vision) to convert into textual labels the content of about 860,000 travel-related pictures posted on Instagram in Summer 2019 for several European islands. The output, a vector of labels’ frequencies on a very fine-grained scale, is used to proxy the destination image at different points in time. We then introduce the Index of Distance in Destination Image, a metric built on the pictures’ labels ranking, and aimed at providing a quantitative measure of (dis)similarity between destination images. We show that the analysis of labels and the index are fit to compare destinations cross-sectionally and over time, providing a useful tool for researchers, marketers and DMOs. We also deliver evidence on how external shocks (like extreme events linked to climate change) or the organization of events modify the cognitive sphere of the destination image, with repercussions on activities undertaken by tourists and relevant implications for local policies.

用户生成内容的可用性以及大数据的开发和机器学习算法为收集和分析大量数据铺平了道路。我们从Instagram上与旅行相关的帖子中扫描图像数据，以识别目标图像的主要特征及其动态性。具体来说，我们利用新推出的视觉对象识别工具（Google Cloud Vision）将2019年夏季发布在Instagram上的约860,000张与旅游有关的图片的内容转换为文本标签，这些内容涉及几个欧洲岛屿。输出是一个非常细粒度的标签频率向量，用于在不同时间点代理目标图像。然后，我们引入“目标图片中的距离索引”，该指标基于图片的标签排名，旨在提供一种定量测量目标图像之间（不相似）相似性的方法。我们显示标签和索引的分析适合于横断面和随时间推移比较目的地，为研究人员，营销人员和DMO提供了有用的工具。我们还提供证据证明外部冲击（例如与气候变化有关的极端事件）或事件的组织如何改变目的地形象的认知范围，并对游客开展的活动产生影响，并对当地政策产生相关影响。