Discovering Scientific Influence using Cross-Domain Dynamic Topic Modeling

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dc.contributor.authorSleeman, Jennifer
dc.contributor.authorFinin, Tim
dc.contributor.authorCane, Mark
dc.contributor.authorHalem, Milton
dc.date.accessioned2018-10-17T16:47:27Z
dc.date.available2018-10-17T16:47:27Z
dc.date.issued2017-01-15
dc.descriptionIEEE International Conference on Big Dataen_US
dc.description.abstractWe describe an approach using dynamic topic modeling to model influence and predict future trends in a scientific discipline. Our study focuses on climate change and uses 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. We use a custom dynamic topic modeling algorithm to generate topics for both datasets and apply crossdomain 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. For the IPCC use case, the report topic model used 410 documents and a vocabulary of 5911 terms while the citations topic model was based on 200K research papers and a vocabulary more than 25K terms. We show that our approach can predict the importance of its extracted topics on future IPCC assessments through the use of cross domain correlations, Jensen-Shannon divergences and cluster analytics.en_US
dc.description.sponsorshipThis work was partially supported by NSF award #1439663 and a gift from IBM.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/8258063en_US
dc.format.extent8 pagesen_US
dc.genreconference paper pre-printen_US
dc.identifierdoi:10.13016/M2JW86R5C
dc.identifier.citationJennifer Sleeman, Tim Finin, Mark Cane, Milton Halem, Discovering Scientific Influence using Cross-Domain Dynamic Topic Modeling, 15 Jan 2017, DOI: 10.1109/BigData.2017.8258063en_US
dc.identifier.uri10.1109/BigData.2017.8258063
dc.identifier.urihttp://hdl.handle.net/11603/11582
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis 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.
dc.rights© 2015 IEEE
dc.subjectbig dataen_US
dc.subjecttopic modelen_US
dc.subjectcross-domain correlationen_US
dc.subjectdata integrationen_US
dc.subjectdomain influenceen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.subjectMeteorologyen_US
dc.subjectdata miningen_US
dc.subjectdata analysisen_US
dc.titleDiscovering Scientific Influence using Cross-Domain Dynamic Topic Modelingen_US
dc.typeTexten_US

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