User-generated content, such as photos, comprises the majority of online media content and drives engagement due to the human ability to process visual information quickly. Consequently, many online platforms are designed for sharing visual content, with billions of photos posted daily. However, photos often reveal more than they intended through visible and contextual cues, leading to privacy risks. Previous studies typically treat privacy as a property of the entire image, overlooking individual objects that may carry varying privacy risks and influence how users perceive it. We address this gap with a mixed-methods study (n = 92) to understand how users evaluate the privacy of images containing multiple sensitive objects. Our results reveal mental models and nuanced patterns that uncover how granular details, such as photo-capturing context and copresence of other objects, affect privacy perceptions. These novel insights could enable personalized, context-aware privacy protection designs on social media and future technologies.

翻译：用户生成内容（如照片）构成了在线媒体内容的主体，并因其能够快速处理视觉信息的人类能力而推动用户参与。因此，许多在线平台专为分享视觉内容而设计，每日发布照片数量达数十亿。然而，照片常常通过可见及上下文线索揭示超出预期的信息，从而引发隐私风险。既往研究通常将隐私视为图像的整体属性，忽视了可能承载不同隐私风险并影响用户感知的个体对象。我们通过一项混合方法研究（n = 92）来填补这一空白，旨在理解用户如何评估包含多个敏感对象的图像隐私。研究结果揭示了心智模型与细微模式，阐明了照片拍摄背景、其他对象共存等细节如何影响隐私感知。这些新颖的发现可为社交媒体及未来技术中个性化、情境感知的隐私保护设计提供依据。