東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Predicting Changes in Public Opinion...

Wirth, Kurt.

FindBook

Google Book

Amazon

博客來

Predicting Changes in Public Opinion with Twitter: What Social Media Data Can and Can't Tell Us About Opinion Formation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predicting Changes in Public Opinion with Twitter: What Social Media Data Can and Can't Tell Us About Opinion Formation./
作者:	Wirth, Kurt.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	93 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-02, Section: A.
Contained By:	Dissertations Abstracts International82-02A.
標題:	Communication. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28026159
ISBN:	9798664730616

Predicting Changes in Public Opinion with Twitter: What Social Media Data Can and Can't Tell Us About Opinion Formation.
Wirth, Kurt.

Predicting Changes in Public Opinion with Twitter: What Social Media Data Can and Can't Tell Us About Opinion Formation. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 93 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: A.

Thesis (Ph.D.)--American University, 2020.

This item must not be sold to any third party vendors.

With the advent of social media data, some researchers have claimed it has the potential to revolutionize the measurement of public opinion. Others have pointed to non-generalizable methods and other concerns to suggest that the role of social media data in the field is limited. Likewise, researchers remain split as to whether automated social media accounts, or bots, have the ability to influence conversations larger than those with their direct audiences. This dissertation examines the relationship between public opinion as measured by random sample surveys, Twitter sentiment, and Twitter bot activity. Analyzing Twitter data on two topics, the president and the economy, as well as daily public polling data, this dissertation offers evidence that changes in Twitter sentiment of the president predict changes in public approval of the president fourteen days later. Likewise, it shows that changes in Twitter bot sentiment of both the president and the economy predict changes in overall Twitter sentiment on those topics between one and two days later. The methods also reveal a previously undiscovered phenomenon by which Twitter sentiment on a topic moves counter to polling approval of the topic at a seven-day interval. This dissertation also discusses the theoretical implications of various methods of calculating social media sentiment. Most importantly, its methods were pre-registered so as to maximize the generalizability of its findings and avoid data cherry-picking or overfitting.

ISBN: 9798664730616Subjects--Topical Terms:

524709
Communication.
Subjects--Index Terms:

Elections

Predicting Changes in Public Opinion with Twitter: What Social Media Data Can and Can't Tell Us About Opinion Formation.
LDR:02733nmm a2200385 4500 001 2274302
005 20201120093413.5
008 220629s2020 ||||||||||||||||| ||eng d
020 $a 9798664730616
035 $a (MiAaPQ)AAI28026159
035 $a AAI28026159
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wirth, Kurt. $3 3551783
245 1 0 $a Predicting Changes in Public Opinion with Twitter: What Social Media Data Can and Can't Tell Us About Opinion Formation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 93 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-02, Section: A.
500 $a Advisor: Menchen-Trevino, Ericka;Campbell, W. Joseph.
502 $a Thesis (Ph.D.)--American University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a With the advent of social media data, some researchers have claimed it has the potential to revolutionize the measurement of public opinion. Others have pointed to non-generalizable methods and other concerns to suggest that the role of social media data in the field is limited. Likewise, researchers remain split as to whether automated social media accounts, or bots, have the ability to influence conversations larger than those with their direct audiences. This dissertation examines the relationship between public opinion as measured by random sample surveys, Twitter sentiment, and Twitter bot activity. Analyzing Twitter data on two topics, the president and the economy, as well as daily public polling data, this dissertation offers evidence that changes in Twitter sentiment of the president predict changes in public approval of the president fourteen days later. Likewise, it shows that changes in Twitter bot sentiment of both the president and the economy predict changes in overall Twitter sentiment on those topics between one and two days later. The methods also reveal a previously undiscovered phenomenon by which Twitter sentiment on a topic moves counter to polling approval of the topic at a seven-day interval. This dissertation also discusses the theoretical implications of various methods of calculating social media sentiment. Most importantly, its methods were pre-registered so as to maximize the generalizability of its findings and avoid data cherry-picking or overfitting.
590 $a School code: 0008.
650 4 $a Communication. $3 524709
650 4 $a Multimedia communications. $3 590562
650 4 $a Mass communications. $3 3422380
653 $a Elections
653 $a Polling
653 $a Prediction
653 $a Public opinion
653 $a Social media
653 $a Twitter
690 $a 0459
690 $a 0708
690 $a 0558
710 2 $a American University. $b School of Communication. $3 3177395
773 0 $t Dissertations Abstracts International $g 82-02A.
790 $a 0008
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28026159