Online photos can reflect tourists’ received destination image and be used to project destination image by destination marketing organizations (DMOs). Studies have identified a gap between projected and received images, highlighting the difficulty DMOs face when selecting content to project the “right” image. Taking an audience-driven perspective, this study analyzed information from user-generated content (UGC) to guide the selection of organization-generated content (OGC) on social media. Using a machine learning algorithm, we extracted connected cognitive and affective elements of received and projected images from UGC and OGC. The elements and their relationships retrieved from UGC were then used to construct a semantic network. The network informs the core–periphery structural information of each element and guides DMOs’ image projection and content selection. Studies with two independent samples demonstrated that an OGC photo whose projected images matched consumers’ central impressions, particularly affective ones, could induce higher online engagement.

在线照片可以反映游客收到的目的地图像，并由目的地营销组织（DMO）用来投影目的地图像。研究已经确定了投影图像和接收图像之间的差距，突显了DMO在选择内容以投影“正确”图像时面临的困难。从受众驱动的角度出发，本研究分析了用户生成内容（UGC）中的信息，以指导在社交媒体上选择组织生成内容（OGC）。使用机器学习算法，我们从UGC和OGC中提取了接收和投影图像的关联认知和情感元素。从UGC检索的元素及其关系随后被用来构建语义网络。该网络告知每个元素的核心-外围结构信息，并指导DMO的图像投影和内容选择。对两个独立样本的研究表明，OGC照片的投影图像与消费者的中心印象（尤其是情感印象）相匹配，可以吸引更高的在线参与度。