Multi-Modal Social Media Analytics: A Sentiment Perception-Driven Framework in Nanjing Districts
This study investigates the complex urban-rural dynamics of Nanjing through a novel multi-modal analysis of 76,288 social media posts, addressing the critical gap in understanding intra-city variations in perceived urban characteristics across rapidly developing Chinese cities. By integrating comput...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10845779/ |
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| author | Meng Xia Zhengyang Lu Feng Wang |
| author_facet | Meng Xia Zhengyang Lu Feng Wang |
| author_sort | Meng Xia |
| collection | DOAJ |
| description | This study investigates the complex urban-rural dynamics of Nanjing through a novel multi-modal analysis of 76,288 social media posts, addressing the critical gap in understanding intra-city variations in perceived urban characteristics across rapidly developing Chinese cities. By integrating computer vision techniques with natural language processing, we develop a comprehensive framework for analyzing public sentiment and attention patterns across eleven districts. Our findings reveal distinct spatial gradients in urban perception that challenge traditional urban-rural dichotomies. While central districts exhibit higher positive sentiments toward built environment (0.65-0.71) and economic factors, peripheral areas show stronger positive associations with environmental quality (0.42) and community cohesion (0.47). Correlation analysis demonstrates significant relationships between socioeconomic indicators and digital engagement patterns, with education levels strongly correlating with cultural heritage attention (r=0.76) and income levels with economic discourse (r=0.94). SHAP analysis further reveals non-linear interaction effects between urban characteristics and public sentiment, particularly in transitional zones. These findings contribute to theories of post-reform Chinese urbanization while offering practical insights for targeted urban planning strategies. The study’s methodological framework provides a replicable approach for analyzing intra-city variations in urban perceptions across rapidly urbanizing regions. |
| format | Article |
| id | doaj-art-2bcd6355b1164d57938d7f9f7c7b34d7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2bcd6355b1164d57938d7f9f7c7b34d72025-01-25T00:01:42ZengIEEEIEEE Access2169-35362025-01-0113126031262210.1109/ACCESS.2025.353176910845779Multi-Modal Social Media Analytics: A Sentiment Perception-Driven Framework in Nanjing DistrictsMeng Xia0Zhengyang Lu1https://orcid.org/0000-0002-1540-0678Feng Wang2https://orcid.org/0009-0003-9217-5245School of Design, Jiangnan University, Wuxi, ChinaSchool of Design, Jiangnan University, Wuxi, ChinaSchool of Design, Jiangnan University, Wuxi, ChinaThis study investigates the complex urban-rural dynamics of Nanjing through a novel multi-modal analysis of 76,288 social media posts, addressing the critical gap in understanding intra-city variations in perceived urban characteristics across rapidly developing Chinese cities. By integrating computer vision techniques with natural language processing, we develop a comprehensive framework for analyzing public sentiment and attention patterns across eleven districts. Our findings reveal distinct spatial gradients in urban perception that challenge traditional urban-rural dichotomies. While central districts exhibit higher positive sentiments toward built environment (0.65-0.71) and economic factors, peripheral areas show stronger positive associations with environmental quality (0.42) and community cohesion (0.47). Correlation analysis demonstrates significant relationships between socioeconomic indicators and digital engagement patterns, with education levels strongly correlating with cultural heritage attention (r=0.76) and income levels with economic discourse (r=0.94). SHAP analysis further reveals non-linear interaction effects between urban characteristics and public sentiment, particularly in transitional zones. These findings contribute to theories of post-reform Chinese urbanization while offering practical insights for targeted urban planning strategies. The study’s methodological framework provides a replicable approach for analyzing intra-city variations in urban perceptions across rapidly urbanizing regions.https://ieeexplore.ieee.org/document/10845779/Social media analyticsurban-rural perceptionsmulti-modal data analysis |
| spellingShingle | Meng Xia Zhengyang Lu Feng Wang Multi-Modal Social Media Analytics: A Sentiment Perception-Driven Framework in Nanjing Districts IEEE Access Social media analytics urban-rural perceptions multi-modal data analysis |
| title | Multi-Modal Social Media Analytics: A Sentiment Perception-Driven Framework in Nanjing Districts |
| title_full | Multi-Modal Social Media Analytics: A Sentiment Perception-Driven Framework in Nanjing Districts |
| title_fullStr | Multi-Modal Social Media Analytics: A Sentiment Perception-Driven Framework in Nanjing Districts |
| title_full_unstemmed | Multi-Modal Social Media Analytics: A Sentiment Perception-Driven Framework in Nanjing Districts |
| title_short | Multi-Modal Social Media Analytics: A Sentiment Perception-Driven Framework in Nanjing Districts |
| title_sort | multi modal social media analytics a sentiment perception driven framework in nanjing districts |
| topic | Social media analytics urban-rural perceptions multi-modal data analysis |
| url | https://ieeexplore.ieee.org/document/10845779/ |
| work_keys_str_mv | AT mengxia multimodalsocialmediaanalyticsasentimentperceptiondrivenframeworkinnanjingdistricts AT zhengyanglu multimodalsocialmediaanalyticsasentimentperceptiondrivenframeworkinnanjingdistricts AT fengwang multimodalsocialmediaanalyticsasentimentperceptiondrivenframeworkinnanjingdistricts |