Extracting Placeness from Social Media an Ontology-Based System

The recent popularity of location-based social (LBS) networking services has resulted in huge volumes of geo-tagged data from social media, allowing us to monitor massive lifelogs from a real-world space. Also, the characteristics of urban areas, placeness, were identified from the lifelogs attained...

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Veröffentlicht in:2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) S. 644 - 651
Hauptverfasser: Choi, Jee Jung, Kim, Jungmin, Park, Heungseok, Lee, Wonjae
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 31.07.2017
Schriftenreihe:ACM Conferences
Schlagworte:
Human-centered computing
Human-centered computing > Visualization
Human-centered computing > Visualization > Visualization application domains
Human-centered computing > Visualization > Visualization application domains > Geographic visualization
Information systems
Information systems > Information retrieval
Information systems > Information retrieval > Document representation
Information systems > Information systems applications
Information systems > Information systems applications > Spatial-temporal systems
Information systems > Information systems applications > Spatial-temporal systems > Geographic information systems
ISBN:1450349935, 9781450349932
ISSN:2473-991X
Online-Zugang:Volltext
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Zusammenfassung:The recent popularity of location-based social (LBS) networking services has resulted in huge volumes of geo-tagged data from social media, allowing us to monitor massive lifelogs from a real-world space. Also, the characteristics of urban areas, placeness, were identified from the lifelogs attained. Based on this concern, in this paper, we propose a new approach of placeness extraction with an ontology-based urban area placeness identification system. The suggested technique uses the textual, temporal, and spatial information of a LBS post from a specific area, and combines this information with the help of ontology. This combination measures the areas occasion-oriented placeness, which can be subdivided into time or companions. Our work focuses on a case study of Twitter data from the city of Seoul. The results show that our system is able to extract subdividable placeness and suitable correspondences when compared to real world socio-geographic features.
ISBN:1450349935
9781450349932
ISSN:2473-991X
DOI:10.1145/3110025.3116198