Advanced Large Scale Cross Domain Temporal Topic Modeling Algorithms to Infer the Influence of Recent Research on IPCC Assessment Reports

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839.pd.pdf (908.33 KB)

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https://ebiquity.umbc.edu/paper/html/id/769/Advanced-Large-Scale-Cross-Domain-Temporal-Topic-Modeling-Algorithms-to-Infer-the-Influence-of-Recent-Research-on-IPCC-Assessment-Reports

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http://hdl.handle.net/11603/11591

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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Date

2016-12-12

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Poster
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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

Big Data
Climate Change
Text Analytics
UMBC Ebiquity Research Group

Abstract

One way of understanding the evolution of science within a particular scientific discipline is by studying the temporal influences that research publications had on that discipline. We provide a methodology for conducting such an analysis by employing cross-domain topic modeling and local cluster mappings of those publications with the historical texts to understand exactly when and how they influenced the discipline. We apply our method to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports and the citations therein. The IPCC reports were compiled by thousands of Earth scientists and the assessments were issued approximately every five years over a 30 year span, and includes over 200,000 research papers cited by these scientists.