Automated Detection of Social Conflict Drivers in Civil Infrastructure Projects Using Natural Language Processing

Abstract

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.