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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01b8515r21j
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dc.contributor.advisorFellbaum, Christiane-
dc.contributor.authorWang, Nina-
dc.date.accessioned2019-07-24T19:56:29Z-
dc.date.available2019-07-24T19:56:29Z-
dc.date.created2019-05-15-
dc.date.issued2019-07-24-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01b8515r21j-
dc.description.abstractAmerican political rhetoric has long been a battleground of "us" versus "them". In this thesis, I use computational methods of text analysis to examine news media during the 2008, 2012, and 2016 election periods, examining how language has been used in public conversation to communicate and reinforce division between political parties. Specifically, this thesis addresses the following questions: (1) Do media outlets use different vocabularies to describe the political party whose ideology aligns with their own, compared to the party on the opposite end of the ideological spectrum? (2) If so, do these different vocabularies also reflect a deeper division along ingroup and outgroup lines? and (3) Do these patterns change over time? I examine these questions using several distinct but complementary methods of textual analysis. First, I examine the extent to which lexical divides are present in the words and topics associated with different political parties. Next, I look at usages of emotion language and language abstractness, evaluating whether divisions are also reinforced semantically, in ways that communicate ingroup and outgroup boundaries. Ultimately, these different methods of analysis work together to paint a broad and multifaceted picture of political polarization in recent years.en_US
dc.format.mimetypeapplication/pdf-
dc.language.isoenen_US
dc.titleNo Neutral Ground: The Language of "Us" and "Them" in Political Media Rhetoricen_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2019en_US
pu.departmentComputer Scienceen_US
pu.pdf.coverpageSeniorThesisCoverPage-
pu.contributor.authorid961135173-
Appears in Collections:Computer Science, 1988-2020

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