Professor Kyuhan Lee of Korea University Business School received the SK Awards for his research proposing an AI model that detects fake news by analyzing human “intent” embedded in text. 

I am honored to receive the SK Award once again. As a researcher, I have always considered it my responsibility to publish in top academic journals, and I have worked diligently toward that goal. Receiving this additional recognition from the university makes it even more meaningful. I am truly grateful for programs such as the SK Award that provide incentives for researchers’ efforts, and I will continue striving to share more of my research with the broader academic community. 

• Paper Title : Intent-Driven Machine Learning for Fake News Detection: A Referential Domain Adaptation Approach  
• Journal : Production and Operations Management 
• Publication Date : January 2026 
• Summary of Key Findings : 

Unlike conventional research in business administration, this study involves the direct development and testing of an AI model. Specifically, it proposes a novel AI model from a sociological perspective that can automatically detect and handle fake news. In particular, the model more accurately estimates the probability that a given news article contains false information by leveraging human intent inferred from the text.

Previous AI research has often struggled to accurately capture intent in text due to limited data. This paper addresses this limitation by proposing and applying a method that utilizes auxiliary data not directly related to intent recognition to infer the intent of news content. When applied in real-world scenarios, the model developed using this methodology produced significantly superior results compared to models built on advanced language models such as GPT.

The findings of this study suggest that developing computational algorithms grounded in social science theory can lead to superior outcomes, and is expected to contribute to the advancement of future research in this area.

▶ View the original paper: Intent-Driven Machine Learning for Fake News Detection: A Referential Domain Adaptation Approach