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Modelling the evolution of climate change research

January 1, 2015 - January 1, 2020

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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Faculty

  1. Tim Finin
  2. Milton Halem

Principal Investigator

  1. Jennifer Sleeman

Publications

2017

  1. J. Sleeman, T. Finin, M. Cane, and M. Halem, "Discovering Scientific Influence using Cross-Domain Dynamic Topic Modeling", InProceedings, IEEE International Conference on Big Data, December 2017, 1033 downloads.
  2. J. Sleeman, "Dynamic Data Assimilation for Topic Modeling (DDATM)", PhdThesis, University of Maryland, Baltimore County, July 2017.
  3. J. Sleeman, M. Halem, T. Finin, and M. Cane, "Modeling the Evolution of Climate Change Assessment Research Using Dynamic Topic Models and Cross-Domain Divergence Maps", InProceedings, AAAI Spring Symposium on AI for Social Good, March 2017, 1951 downloads.

2016

  1. J. Sleeman, M. Halem, T. Finin, and M. Cane, "Advanced Large Scale Cross Domain Temporal Topic Modeling Algorithms to Infer the Influence of Recent Research on IPCC Assessment Reports", InProceedings, American Geophysical Union Fall Meeting 2016, December 2016, 1642 downloads.
  2. J. Sleeman, M. Halem, T. Finin, and M. Cane, "Dynamic Topic Modeling to Infer the Influence of Research Citations on IPCC Assessment Reports", InProceedings, Big Data Challenges, Research, and Technologies in the Earth and Planetary Sciences Workshop, IEEE Int. Conf. on Big Data, December 2016, 1384 downloads.