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A network-based approach to estimating partisanship and analyzing change in polarization during the 2016 general election
Kearney, Michael Wayne
Kearney, Michael Wayne
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Abstract
Communication and media research lacks an accessible and systematic approach to measuring political partisanship in decentralized media environments. In this dissertation, a network-based measurement of partisanship is proposed and then used to analyze social media users during a highly contentious general election. Study I (Chapter 2) introduces rtweet, a newly developed open-source software package designed to collect Twitter data. Study II (Chapter 3) then uses rtweet to gather publicly available Twitter data and demonstrate a network-based approach to estimating partisanship. Finally, Study 3 (Chapter 4) extends this network-based approach to analyze change over time in network polarization among partisan and non-partisan users during the 2016 general election. Results showcase the range and validity of network-based estimates of partisanship and provide clear evidence of partisan selective exposure and network polarization on Twitter as proximity to the election increases.
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Date
2017-05-31
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University of Kansas
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Keywords
Mass communication, Communication, Political science, networks, partisanship, polarization, selective exposure, twitter