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Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review

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  • Chenghao Yang

    (Department of Architecture, School of Architecture, Tianjin University, Tianjin 300072, China
    Department of Architecture, College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore)

  • Tongtong Liu

    (Department of Architecture, School of Architecture, Tianjin University, Tianjin 300072, China)

Abstract

Social media data have been widely used in natural sciences and social sciences in the past 5 years, benefiting from the rapid development of deep learning frameworks and Web 2.0. Its advantages have gradually emerged in urban design, urban planning, landscape architecture design, sustainable tourism, and other disciplines. This study aims to obtain an overview of social media data in urban design and landscape research through literature reviews and bibliometric visualization as a comprehensive review article. The dataset consists of 1220 articles and reviews works from SSCI, SCIE, and A&HCI, based on the Web of Science core collection, respectively. The research progress and main development directions of location-based social media, text mining, and image vision are introduced. Moreover, we introduce Citespace, a computer-network-based bibliometric visualization, and discuss the timeline trends, hot burst keywords, and research articles with high co-citation scores based on Citespace. The Citespace bibliometric visualization tool facilitates is used to outline future trends in research. The literature review shows that the deep learning framework has great research potential for text emotional analysis, image classification, object detection, image segmentation, and the expression classification of social media data. The intersection of text, images, and metadata provides attractive opportunities as well.

Suggested Citation

  • Chenghao Yang & Tongtong Liu, 2022. "Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review," Land, MDPI, vol. 11(10), pages 1-22, October.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:10:p:1796-:d:942259
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    References listed on IDEAS

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    2. Ruochen Ma & Katsunori Furuya, 2024. "Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review," Land, MDPI, vol. 13(2), pages 1-22, February.

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