Location‐based social networks (LBSNs) have become an important source of spatial data for geographers and GIScientists to acquire knowledge of human–place interactions. A number of studies have used geotagged data from LBSNs to investigate how user‐generated content (UGC) can be affected by or correlated with the external environment. However, local visual information at the micro‐level, such as brightness, colorfulness, or particular objects/events in the surrounding environment, is usually not captured and thus becomes a missing component in LBSN analysis. To provide a solution to this issue, we argue in this study that the integration of augmented reality (AR) and LBSNs proves to be a promising avenue. In this first empirical study on AR‐based LBSNs, we propose a methodological framework to extract and analyze data from AR‐based LBSNs and demonstrate the framework via a case study with WallaMe. Our findings bolster existing psychological findings on the color–mood relationship and display intriguing geographic patterns of the influence of local visual information on UGC in social media.

中文翻译：

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使用基于增强现实的基于位置的社交网络分析用户生成的内容与本地视觉信息之间的关系

基于位置的社交网络（LBSN）已成为地理学家和GIS科学家获取人地交互知识的重要空间数据来源。许多研究使用了来自LBSN的地理标记数据来研究用户生成的内容（UGC）如何受到外部环境的影响或与外部环境相关联。但是，通常无法捕获微观级别的局部视觉信息，例如亮度，色彩或周围环境中的特定对象/事件，因此成为LBSN分析中缺少的组成部分。为了提供对此问题的解决方案，我们在本研究中认为增强现实（AR）和LBSN的集成被证明是一种有前途的途径。在基于AR的LBSN的第一个实证研究中，我们提出了一种方法框架，用于从基于AR的LBSN中提取和分析数据，并通过与WallaMe进行案例研究来演示该框架。我们的研究结果支持了现有的关于色觉-情绪关系的心理学研究结果，并显示了社交媒体中本地视觉信息对UGC影响的有趣的地理模式。