## 7 Conclusions  
This study presented an automated process and method for detecting conflict drivers using ChatGPT. The authors developed a web crawler to collect news articles, which are data sources for identifying conflict drivers. Using project names as queries, this study collected online news articles related to civil infrastructure projects implemented in South Korea. Then, this study utilized ChatGPT to extract conflict-related keyphrases from the article collections and classify the extracted keyphrases into predefined conflict drivers. The performance of KPC was measured by micro, macro, and weighted average F1-scores  
Figure 4: Frequency of the dominant conflict drivers by quarter in the Cheonseong Mountain Tunnel case. of 85.7%, 83.6%, and 84.7%, respectively. In particular, the recall values of 15 out of the 18 conflict drivers were above 90%, and the recall values of all but 4 conflict drivers were calculated to be greater than or equal to the precision values. This indicates that the output of ChatGPT-based KPC is useful for practical conflict analysis. To evaluate the performance of KPE, this study conducted two illustrative case studies using actual projects implemented in South Korea. As a result, the extracted keyphrases satisfactorily described the actual conflict phenomena. The results of this study contribute to performing timely data-driven conflict management. The occurrence of conflict during the project implementation leads to cost overruns and schedule delays. If the conflict intensifies and spreads nationwide, it may end up with project cancellation. Therefore, it is crucial to identify and hedge conflict drivers as early as possible for the success of the project. Nevertheless, to the best of the authors' knowledge, there is no scientific system for the timely monitoring and management of conflicts in practice. Using the process and methods presented in this study, practitioners can quickly identify which conflict drivers are emerging and what the point of the issue is based on up-to-date news. In addition, this study contributes to the body of knowledge in conflict management by laying the groundwork for facilitating data-driven conflict analysis. The qualitative approaches of previous studies have limitations in generalizing based on comprehensive analysis. By using a large amount of news data and state-of-the-art NLP technology, researchers would be able to study the conflicts more efficiently. The authors expect the result of this study will prime the pump for data-driven conflict analysis in the civil engineering and management domain by overcoming the limitations of previous qualitative approaches. There is also room for improvement in this study. First, the degree of news digitalization was not considered. When collecting historical news data, the frequency of news publication varies depending on the activity level of online news. It is necessary to adjust the frequency-based significance of conflict drivers by considering the publication time. Second, the identification of other conflict-related factors is required for in-depth conflict analysis, such as stakeholder type, specific conflict events, and conflict impact on project performance. Third, this study collected news data from a major portal site, which may omit smaller news media. Empirically, residents' opinions and perceptions toward nearby construction projects are often first expressed in local news media. It is more difficult to mitigate conflicts as they intensify. Therefore, it is necessary to expand data sources to include local news media in order to detect earlier signals of latent conflict. In future research, the authors will focus on quantifying the conflict index based on the results of this study by complementing the aforementioned limitations. Thus, subsequent studies will expand the body of knowledge on conflict management from qualitative to quantitative analyses and from case studies to data-driven studies. Conceptualization, S.B., D.N., J.W. and S.H.H. ; methodology, S.B. and D.N. ; data curation, D.N.