Abstract
The rapid expansion of artificial intelligence (AI) is documented in heterogeneous sources that differ in terminology, granularity, and temporal patterns. Existing scientometric approaches often rely on single-source datasets and flat topic labels, limiting their ability to capture multi-level conceptual change or differences between communities. This paper introduces a metric-based framework for analyzing topic trends across heterogeneous sources using hierarchical taxonomies. Rather than proposing a new taxonomy or labelling method, we show how hierarchical topic structures can support aggregation and comparison across levels of abstraction. The framework provides general metrics quantifying topic popularity, granularity, diversity, temporal dynamics, and cross-source temporal uniqueness. We illustrate the approach using three contemporary sources (arXiv, Hugging Face, and the Deep Learning Weekly newsletter) mapped to a taxonomy through an adapted labelling pipeline. The analyses highlight distinct patterns in topical focus, detail depth, and temporal development across sources. The framework is extensible and offers a foundation for scalable, and multi-source monitoring of AI’s evolving landscape. In addition, an interactive tool was developed to assist exploration of the trend analysis results.