Two Computational Models for Analyzing Political Attention in Social Media

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dc.contributor.author Hemphill, Libby
dc.contributor.author Schöpke-Gonzalez, Angela
dc.date.accessioned 2019-02-01T16:46:50Z
dc.date.available 2019-02-01T16:46:50Z
dc.date.issued 2019
dc.identifier.uri http://hdl.handle.net/2027.42/147460
dc.description.abstract Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. However, existing methods for measuring attention, such as manual labeling ac- cording to established codebooks, are expensive and restric- tive. We describe two computational models that automati- cally distinguish topics in politicians’ social media content. Our models - one supervised classifier and one unsupervised topic model - provide different benefits. The supervised clas- sifier reduces the labor required to classify content accord- ing to pre-determined topic lists. However, tweets do more than communicate policy positions. Our unsupervised model uncovers both political topics and other Twitter uses (e.g., constituent service). Together, these models are effective, in- expensive computational tools for political communication and social media research. We demonstrate their utility and discuss the different analyses they afford by applying both models to the tweets posted by members of the 115th U.S. Congress. en_US
dc.description.sponsorship This material is based upon work supported by the National Science Foundation under Grant No. 1822228. en_US
dc.language.iso en_US en_US
dc.rights CC0 1.0 Universal *
dc.rights.uri http://creativecommons.org/publicdomain/zero/1.0/ *
dc.subject twitter en_US
dc.subject political communication en_US
dc.subject machine learning en_US
dc.subject US Congress en_US
dc.title Two Computational Models for Analyzing Political Attention in Social Media en_US
dc.type Article en_US
dc.subject.hlbsecondlevel Information and Library Science
dc.subject.hlbtoplevel Social Sciences
dc.contributor.affiliationum Information, School of en_US
dc.contributor.affiliationum ICSPR en_US
dc.contributor.affiliationumcampus Ann Arbor en_US
dc.description.bitstreamurl https://deepblue.lib.umich.edu/bitstream/2027.42/147460/6/Hemphill and Schopke - Two Compuational Models.pdf
dc.description.bitstreamurl https://deepblue.lib.umich.edu/bitstream/2027.42/147460/1/Hemphill and Schopke - Two Computational Models.pdf
dc.description.bitstreamurl https://deepblue.lib.umich.edu/bitstream/2027.42/147460/8/ICWSM 2020 Two Computational Models.pptx
dc.description.mapping 50 en_US
dc.description.mapping 56 en_US
dc.identifier.orcid 0000-0002-3793-7281 en_US
dc.description.filedescription Description of Hemphill and Schopke - Two Compuational Models.pdf : Revised article
dc.description.filedescription Description of Hemphill and Schopke - Two Computational Models.pdf : Main article
dc.description.filedescription Description of ICWSM 2020 Two Computational Models.pptx : Presentation with script
dc.identifier.name-orcid Hemphill, Libby; 0000-0002-3793-7281 en_US
dc.owningcollname Information, School of (SI)
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