Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review
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 touris...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/11/10/1796 |
| _version_ | 1797472069118590976 |
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| author | Chenghao Yang Tongtong Liu |
| author_facet | Chenghao Yang Tongtong Liu |
| author_sort | Chenghao Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:57:01Z |
| format | Article |
| id | doaj.art-7c10322295f24cd5a8fc8c4096c64889 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-09T19:57:01Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-7c10322295f24cd5a8fc8c4096c648892023-11-24T00:54:07ZengMDPI AGLand2073-445X2022-10-011110179610.3390/land11101796Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature ReviewChenghao Yang0Tongtong Liu1Department of Architecture, School of Architecture, Tianjin University, Tianjin 300072, ChinaDepartment of Architecture, School of Architecture, Tianjin University, Tianjin 300072, ChinaSocial 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.https://www.mdpi.com/2073-445X/11/10/1796social media datalocation-based social medianatural language processingcomputer visionCitespace |
| spellingShingle | Chenghao Yang Tongtong Liu Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review Land social media data location-based social media natural language processing computer vision Citespace |
| title | Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review |
| title_full | Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review |
| title_fullStr | Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review |
| title_full_unstemmed | Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review |
| title_short | Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review |
| title_sort | social media data in urban design and landscape research a comprehensive literature review |
| topic | social media data location-based social media natural language processing computer vision Citespace |
| url | https://www.mdpi.com/2073-445X/11/10/1796 |
| work_keys_str_mv | AT chenghaoyang socialmediadatainurbandesignandlandscaperesearchacomprehensiveliteraturereview AT tongtongliu socialmediadatainurbandesignandlandscaperesearchacomprehensiveliteraturereview |