Automated Detection of Social Conflict Drivers in Civil Infrastructure Projects Using Natural Language Processing
Early detection and mitigation of social conflict in civil infrastructure projects is essential due to its significant impact on project performance and social governance. Nevertheless, there is no scientific system for monitoring conflict drivers in a timely manner in practice. Furthermore, previou...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/20/11171 |
| _version_ | 1827721908505804800 |
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| author | Seungwon Baek Do Namgoong Jinwoo Won Seung H. Han |
| author_facet | Seungwon Baek Do Namgoong Jinwoo Won Seung H. Han |
| author_sort | Seungwon Baek |
| collection | DOAJ |
| description | Early detection and mitigation of social conflict in civil infrastructure projects is essential due to its significant impact on project performance and social governance. Nevertheless, there is no scientific system for monitoring conflict drivers in a timely manner in practice. Furthermore, previous studies of social conflict in the civil engineering and management domains have relied on manual literature reviews and case studies. Although these qualitative approaches have provided context-specific insights, they are limited in their generalizability and broad perspectives. Against this backdrop, this study presents an automated process for detecting conflict drivers from news articles using ChatGPT. The authors collected news articles related to civil infrastructure projects implemented in the Republic of Korea using web crawling. Then, ChatGPT was used to extract conflict-related keyphrases from the article collections and classify the keyphrases into predefined conflict drivers. The result showed a notable performance with a micro average F1-score of 85.7%. Moreover, the authors confirmed the validity of the keyphrase extraction and classification results through two illustrative case studies. The proposed process and methods contribute to facilitating data-driven conflict management. Although this study focused on conflict drivers of public infrastructure projects, other types of information extraction tasks can benefit from the presented framework. |
| first_indexed | 2024-03-10T21:28:22Z |
| format | Article |
| id | doaj.art-6a081a752cc24182a9f258c18c3c456e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:28:22Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6a081a752cc24182a9f258c18c3c456e2023-11-19T15:29:31ZengMDPI AGApplied Sciences2076-34172023-10-0113201117110.3390/app132011171Automated Detection of Social Conflict Drivers in Civil Infrastructure Projects Using Natural Language ProcessingSeungwon Baek0Do Namgoong1Jinwoo Won2Seung H. Han3Department of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaDepartment of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaDepartment of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaDepartment of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaEarly detection and mitigation of social conflict in civil infrastructure projects is essential due to its significant impact on project performance and social governance. Nevertheless, there is no scientific system for monitoring conflict drivers in a timely manner in practice. Furthermore, previous studies of social conflict in the civil engineering and management domains have relied on manual literature reviews and case studies. Although these qualitative approaches have provided context-specific insights, they are limited in their generalizability and broad perspectives. Against this backdrop, this study presents an automated process for detecting conflict drivers from news articles using ChatGPT. The authors collected news articles related to civil infrastructure projects implemented in the Republic of Korea using web crawling. Then, ChatGPT was used to extract conflict-related keyphrases from the article collections and classify the keyphrases into predefined conflict drivers. The result showed a notable performance with a micro average F1-score of 85.7%. Moreover, the authors confirmed the validity of the keyphrase extraction and classification results through two illustrative case studies. The proposed process and methods contribute to facilitating data-driven conflict management. Although this study focused on conflict drivers of public infrastructure projects, other types of information extraction tasks can benefit from the presented framework.https://www.mdpi.com/2076-3417/13/20/11171civil infrastructure projectconflict driverkeyphrase extractionkeyphrase classificationnatural language processingChatGPT |
| spellingShingle | Seungwon Baek Do Namgoong Jinwoo Won Seung H. Han Automated Detection of Social Conflict Drivers in Civil Infrastructure Projects Using Natural Language Processing Applied Sciences civil infrastructure project conflict driver keyphrase extraction keyphrase classification natural language processing ChatGPT |
| title | Automated Detection of Social Conflict Drivers in Civil Infrastructure Projects Using Natural Language Processing |
| title_full | Automated Detection of Social Conflict Drivers in Civil Infrastructure Projects Using Natural Language Processing |
| title_fullStr | Automated Detection of Social Conflict Drivers in Civil Infrastructure Projects Using Natural Language Processing |
| title_full_unstemmed | Automated Detection of Social Conflict Drivers in Civil Infrastructure Projects Using Natural Language Processing |
| title_short | Automated Detection of Social Conflict Drivers in Civil Infrastructure Projects Using Natural Language Processing |
| title_sort | automated detection of social conflict drivers in civil infrastructure projects using natural language processing |
| topic | civil infrastructure project conflict driver keyphrase extraction keyphrase classification natural language processing ChatGPT |
| url | https://www.mdpi.com/2076-3417/13/20/11171 |
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