]> We are developing algorithms using dynamic topic modeling to understand influence and predict future trends in a scientific discipline. As an initial use case, we are applying this to climate change and use assessment reports of the Intergovernmental Panel on Climate Change (IPCC) and the papers they cite. Since 1990, an IPCC report has been published every five years that includes four separate volumes, each of which has many chapters. Each report cites tens of thousands of research papers, which comprise a correlated dataset of temporally grounded documents. Our custom dynamic topic modeling algorithm identified topics for both datasets and apply cross-domain analytics to identify the correlations between the IPCC chapters and their cited documents. The approach reveals both the influence of the cited research on the reports and how previous research citations have evolved over time.

This project is partially supported by a grant from Microsoft's AI for Earth program.

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