Data set - Exploring YouTube’s Algorithmic Patterns in K-Pop Album Video Recommendations

The data were collected using a snowball sampling technique on YouTube, starting from specific K-pop album-related keywords and following the chain of recommended videos generated by the platform. Each recommended video was documented, including video titles, subscriber counts, views, and timestamps of the top fifteen recommendations at each level of exploration to analyze patterns in how YouTube’s algorithm circulates K-pop album content.