Abstract
ABSTRACTThe social functionality of places (e.g. school, restaurant) partly determines human behaviors and reflects a region’s functional configuration. Semantic descriptions of places are thus valuable to a range of studies of humans and geographic spaces. Assuming their potential impacts on human verbalization behaviors, one possibility is to link the functions of places to verbal representations such as users’ postings in location-based social networks (LBSNs). In this study, we examine whether the heterogeneous user-generated text snippets found in LBSNs reliably reflect the semantic concepts attached with check-in places. We investigate Foursquare because its available categorization hierarchy provides rich a-priori semantic knowledge about its check-in places, which enables a reliable verification of the semantic concepts identified from user-generated text snippets. A latent semantic analysis is conducted on a large Foursquare check-in dataset. The results confirm that attached text messages can represent semantic concepts by demonstrating their large correspondence to the official Foursquare venue categorization. To further elaborate the representativeness of text messages, this work also performs an investigation on the textual terms to quantify their abilities of representing semantic concepts (i.e., representativeness), and another investigation on semantic concepts to quantify how well they can be represented by text messages (i.e., representability). The results shed light on featured terms with strong locational characteristics, as well as on distinctive semantic concepts with potentially strong impacts on human verbalizations.
Highlights
Considerable technological advancements in recent years have made possible the widespread use of location-based social networks (LBSNs) like the check-in service Foursquare
We examine whether the heterogeneous user-generated text snippets found in LBSNs reliably reflect the semantic concepts attached with check-in places
We investigate Foursquare because its available categorization hierarchy provides rich a-priori semantic knowledge about its check-in places, which enables a reliable verification of the semantic concepts identified from user-generated text snippets
Summary
Considerable technological advancements in recent years have made possible the widespread use of location-based social networks (LBSNs) like the check-in service Foursquare ( called Swarm). To the folksonomy representing the thematic dimensions of the OpenStreetMap project (Mocnik, Zipf, and Raifer 2017), the Foursquare venue categories are generated by users and, after adoption by Foursquare, organized into a predefined categorization hierarchy This way, the well-organized information of venue categories provides a-priori semantic knowledge, which can be used to validate the identified semantic concepts from the text messages. This makes Foursquare well suited for the purpose of this study, which is to examine whether (and how well) the user-generated text messages in LBSNs represent semantic concepts of locations. The paper closes with a discussion of the results and by drawing a conclusion
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