Analyzing relationship between user‐generated content and local visual information with augmented reality‐based location‐based social networks

Abstract

Abstract 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.

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