Using social media for environmental insight: a multi-model deep learning framework approach
Monitoring ecological conditions and detecting environmental issues are critical for safeguarding human health and sustainable development. Previous studies have shown that social media data can complement traditional methods, such as remote sensing, by capturing public sentiment; however, existing...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2541877 |
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| author | Minglu Che Chengyao Wang Yanyun Nian Pinqi Rao |
| author_facet | Minglu Che Chengyao Wang Yanyun Nian Pinqi Rao |
| author_sort | Minglu Che |
| collection | DOAJ |
| description | Monitoring ecological conditions and detecting environmental issues are critical for safeguarding human health and sustainable development. Previous studies have shown that social media data can complement traditional methods, such as remote sensing, by capturing public sentiment; however, existing text-processing frameworks for social media face challenges in constructing comprehensive keyword lexicons and achieving efficient information extraction. To address these limitations, this study proposes a multi-model Weibo text-processing framework for environmental issues. An iterative expansion strategy was employed to rapidly develop an extensive environmental topic lexicon, and a named entity recognition model based on the BERT – BiGRU – CRF architecture was implemented to extract environmental and geographic keywords with high efficiency. Using this framework, we built a lexicon of 1,019 environmental topic keywords and collected 47,737 posts from Weibo between June 2023 and June 2024, each of which was analyzed for topic classification, geographic location, and sentiment polarity. The experimental results demonstrate that the framework achieves F1 scores of 0.90 for topic classification, 0.80 for sentiment analysis, and over 0.87 for both environmental and location keyword recognition. Significant negative sentiment was detected in Beijing and Chengdu, while the Shandong – Jiangsu region exhibited consistently strong positive sentiment. High-frequency keyword analysis revealed that air pollution (31.54%), environmental protection (14.85%), and ecological destruction (13.09%) were the top concerns. Water pollution case studies further validate the framework’s ability to rapidly localize pollution events. Overall, the proposed framework significantly enhances the timeliness and accuracy of environmental public opinion monitoring, providing novel insights and decision-support capabilities for policymakers. |
| format | Article |
| id | doaj-art-ef93dd4ec2004cd2a5976de4a4334821 |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-ef93dd4ec2004cd2a5976de4a43348212025-08-20T03:41:04ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-08-0111610.1080/10095020.2025.2541877Using social media for environmental insight: a multi-model deep learning framework approachMinglu Che0Chengyao Wang1Yanyun Nian2Pinqi Rao3Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, ChinaKey Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, ChinaKey Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, ChinaKey Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, ChinaMonitoring ecological conditions and detecting environmental issues are critical for safeguarding human health and sustainable development. Previous studies have shown that social media data can complement traditional methods, such as remote sensing, by capturing public sentiment; however, existing text-processing frameworks for social media face challenges in constructing comprehensive keyword lexicons and achieving efficient information extraction. To address these limitations, this study proposes a multi-model Weibo text-processing framework for environmental issues. An iterative expansion strategy was employed to rapidly develop an extensive environmental topic lexicon, and a named entity recognition model based on the BERT – BiGRU – CRF architecture was implemented to extract environmental and geographic keywords with high efficiency. Using this framework, we built a lexicon of 1,019 environmental topic keywords and collected 47,737 posts from Weibo between June 2023 and June 2024, each of which was analyzed for topic classification, geographic location, and sentiment polarity. The experimental results demonstrate that the framework achieves F1 scores of 0.90 for topic classification, 0.80 for sentiment analysis, and over 0.87 for both environmental and location keyword recognition. Significant negative sentiment was detected in Beijing and Chengdu, while the Shandong – Jiangsu region exhibited consistently strong positive sentiment. High-frequency keyword analysis revealed that air pollution (31.54%), environmental protection (14.85%), and ecological destruction (13.09%) were the top concerns. Water pollution case studies further validate the framework’s ability to rapidly localize pollution events. Overall, the proposed framework significantly enhances the timeliness and accuracy of environmental public opinion monitoring, providing novel insights and decision-support capabilities for policymakers.https://www.tandfonline.com/doi/10.1080/10095020.2025.2541877Environmental monitoringnatural language processingdeep learning |
| spellingShingle | Minglu Che Chengyao Wang Yanyun Nian Pinqi Rao Using social media for environmental insight: a multi-model deep learning framework approach Geo-spatial Information Science Environmental monitoring natural language processing deep learning |
| title | Using social media for environmental insight: a multi-model deep learning framework approach |
| title_full | Using social media for environmental insight: a multi-model deep learning framework approach |
| title_fullStr | Using social media for environmental insight: a multi-model deep learning framework approach |
| title_full_unstemmed | Using social media for environmental insight: a multi-model deep learning framework approach |
| title_short | Using social media for environmental insight: a multi-model deep learning framework approach |
| title_sort | using social media for environmental insight a multi model deep learning framework approach |
| topic | Environmental monitoring natural language processing deep learning |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2025.2541877 |
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