東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

A Network-Based Approach to Estimati...

Kearney, Michael W.

Linked to FindBook

Google Book

Amazon

博客來

A Network-Based Approach to Estimating Partisanship and Analyzing Change in Polarization During the 2016 General Election.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Network-Based Approach to Estimating Partisanship and Analyzing Change in Polarization During the 2016 General Election./
Author:	Kearney, Michael W.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	106 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: A.
Contained By:	Dissertation Abstracts International79-04A(E).
Subject:	Mass communication. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10272822
ISBN:	9780355342895

A Network-Based Approach to Estimating Partisanship and Analyzing Change in Polarization During the 2016 General Election.
Kearney, Michael W.

A Network-Based Approach to Estimating Partisanship and Analyzing Change in Polarization During the 2016 General Election. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 106 p.

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: A.

Thesis (Ph.D.)--University of Kansas, 2017.

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.

ISBN: 9780355342895Subjects--Topical Terms:

2144804
Mass communication.

A Network-Based Approach to Estimating Partisanship and Analyzing Change in Polarization During the 2016 General Election.
LDR:02036nmm a2200325 4500 001 2155407
005 20180426091046.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355342895
035 $a (MiAaPQ)AAI10272822
035 $a (MiAaPQ)ku:15182
035 $a AAI10272822
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kearney, Michael W. $3 3343145
245 1 2 $a A Network-Based Approach to Estimating Partisanship and Analyzing Change in Polarization During the 2016 General Election.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 106 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: A.
500 $a Advisers: Mary C. Banwart; Jeff A. Hall.
502 $a Thesis (Ph.D.)--University of Kansas, 2017.
520 $a 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.
590 $a School code: 0099.
650 4 $a Mass communication. $3 2144804
650 4 $a Communication. $3 524709
650 4 $a Political science. $3 528916
650 4 $a Web studies. $3 2122754
690 $a 0708
690 $a 0459
690 $a 0615
690 $a 0646
710 2 $a University of Kansas. $b Communication Studies. $3 1020359
773 0 $t Dissertation Abstracts International $g 79-04A(E).
790 $a 0099
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10272822