Stack Overflow is a vital platform for developers to exchange knowledge, yet its extensive content creates challenges such as information overload and inconsistent quality. While previous research has focused mainly on assessing content quality, effectively identifying high-value and low-value content remains essential for improving information retrieval. Information Foraging Theory (IFT) provides a useful framework for understanding developers’ information-seeking behaviors in information-rich environments like StackOverflow. Building on prior research applying IFT to Q&A websites, this study introduces an IFT-enhanced agglomerative hierarchical clustering approach, classifying 15,000 StackOverflow questions and 15,000 answers into high-value, low-value, high-cost, and low-cost clusters. Results indicate that high-value content is characterized by higher upvotes, greater author reputation, and increased engagement, whereas high-cost content tends to have longer descriptions and additional metadata cues. These findings demonstrate that IFT-driven clustering effectively distinguishes content clusters based on developer-perceived value and cost, offering insights for refining recommendation systems and optimizing information retrieval.